Geiser C. Challco geiser@alumni.usp.br
NOTE:
dv = "vocab.teach"
dv.pos = "vocab.teach.pos"
dv.pre = "vocab.teach.pre"
fatores2 <- c("Sexo","Zona","Cor.Raca","Serie","vocab.teach.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#008000")
color[["Sexo"]] = c("#FF007F","#4D4DFF")
color[["Zona"]] = c("#AA00FF","#00CCCC")
color[["Cor.Raca"]] = c(
"Parda"="#b97100","Indígena"="#9F262F",
"Branca"="#87c498", "Preta"="#848283","Amarela"="#D6B91C"
)
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["Sexo"]] = c("F","M")
level[["Zona"]] = c("Rural","Urbana")
level[["Cor.Raca"]] = c("Parda","Indígena","Branca", "Preta","Amarela")
level[["Serie"]] = c("6 ano","7 ano","8 ano","9 ano")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:Sexo"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:Zona"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:Cor.Raca"]] = c(
"Controle:Parda"="#e3c699", "Experimental:Parda"="#b97100",
"Controle:Indígena"="#e2bdc0", "Experimental:Indígena"="#9F262F",
"Controle:Branca"="#c0e8cb", "Experimental:Branca"="#87c498",
"Controle:Preta"="#dad9d9", "Experimental:Preta"="#848283",
"Controle:Amarela"="#eee3a4", "Experimental:Amarela"="#D6B91C",
"Controle.Parda"="#e3c699", "Experimental.Parda"="#b97100",
"Controle.Indígena"="#e2bdc0", "Experimental.Indígena"="#9F262F",
"Controle.Branca"="#c0e8cb", "Experimental.Branca"="#87c498",
"Controle.Preta"="#dad9d9", "Experimental.Preta"="#848283",
"Controle.Amarela"="#eee3a4", "Experimental.Amarela"="#D6B91C"
)
for (coln in c("vocab","vocab.teach","vocab.non.teach","score.tde",
"TFL.lidas.per.min","TFL.corretas.per.min","TFL.erradas.per.min","TFL.omitidas.per.min",
"leitura.compreensao")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
tdat <- read_excel("../data/data.xlsx", sheet = "sumary")
tdat <- tdat[!is.na(tdat[["WG.Grupo"]]),]
tdat$grupo <- factor(tdat[["WG.Grupo"]], level[["grupo"]])
gdat <- tdat[which(is.na(tdat$Necessidade.Deficiencia) & !is.na(tdat$WG.Grupo)),]
dat <- gdat
dat$grupo <- factor(dat[["WG.Grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.
## There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| grupo | Sexo | Zona | Cor.Raca | Serie | vocab.teach.quintile | variable | n | mean | median | min | max | sd | se | ci | iqr | symmetry | skewness | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Controle | vocab.teach.pre | 516 | 4.506 | 4.0 | 0 | 13 | 2.164 | 0.095 | 0.187 | 3.00 | YES | 0.358 | -0.156 | |||||
| Experimental | vocab.teach.pre | 713 | 4.759 | 5.0 | 0 | 13 | 2.018 | 0.076 | 0.148 | 3.00 | YES | 0.383 | -0.021 | |||||
| vocab.teach.pre | 1229 | 4.653 | 4.0 | 0 | 13 | 2.083 | 0.059 | 0.117 | 3.00 | YES | 0.358 | -0.073 | ||||||
| Controle | vocab.teach.pos | 516 | 4.919 | 5.0 | 0 | 15 | 2.294 | 0.101 | 0.198 | 3.00 | YES | 0.295 | 0.106 | |||||
| Experimental | vocab.teach.pos | 713 | 5.401 | 5.0 | 0 | 12 | 2.273 | 0.085 | 0.167 | 3.00 | YES | 0.206 | -0.390 | |||||
| vocab.teach.pos | 1229 | 5.199 | 5.0 | 0 | 15 | 2.293 | 0.065 | 0.128 | 3.00 | YES | 0.237 | -0.188 | ||||||
| Controle | F | vocab.teach.pre | 259 | 4.587 | 5.0 | 0 | 10 | 2.032 | 0.126 | 0.249 | 3.00 | YES | 0.139 | -0.532 | ||||
| Controle | M | vocab.teach.pre | 257 | 4.424 | 4.0 | 0 | 13 | 2.290 | 0.143 | 0.281 | 3.00 | NO | 0.531 | 0.058 | ||||
| Experimental | F | vocab.teach.pre | 368 | 4.777 | 5.0 | 0 | 13 | 2.063 | 0.108 | 0.211 | 3.00 | YES | 0.375 | 0.137 | ||||
| Experimental | M | vocab.teach.pre | 345 | 4.739 | 5.0 | 1 | 11 | 1.971 | 0.106 | 0.209 | 3.00 | YES | 0.387 | -0.257 | ||||
| Controle | F | vocab.teach.pos | 259 | 5.058 | 5.0 | 0 | 15 | 2.303 | 0.143 | 0.282 | 2.00 | YES | 0.337 | 0.675 | ||||
| Controle | M | vocab.teach.pos | 257 | 4.778 | 5.0 | 0 | 11 | 2.281 | 0.142 | 0.280 | 3.00 | YES | 0.249 | -0.536 | ||||
| Experimental | F | vocab.teach.pos | 368 | 5.698 | 6.0 | 0 | 12 | 2.249 | 0.117 | 0.230 | 3.00 | YES | 0.212 | -0.394 | ||||
| Experimental | M | vocab.teach.pos | 345 | 5.084 | 5.0 | 0 | 12 | 2.258 | 0.122 | 0.239 | 4.00 | YES | 0.214 | -0.426 | ||||
| Controle | Rural | vocab.teach.pre | 253 | 4.462 | 4.0 | 0 | 11 | 2.069 | 0.130 | 0.256 | 3.00 | YES | 0.324 | -0.388 | ||||
| Controle | Urbana | vocab.teach.pre | 114 | 4.412 | 4.0 | 0 | 10 | 1.968 | 0.184 | 0.365 | 2.75 | YES | 0.306 | -0.024 | ||||
| Controle | vocab.teach.pre | 149 | 4.651 | 5.0 | 0 | 13 | 2.452 | 0.201 | 0.397 | 3.00 | YES | 0.343 | -0.289 | |||||
| Experimental | Rural | vocab.teach.pre | 294 | 4.694 | 5.0 | 0 | 11 | 2.068 | 0.121 | 0.237 | 3.00 | YES | 0.289 | -0.366 | ||||
| Experimental | Urbana | vocab.teach.pre | 189 | 4.815 | 5.0 | 1 | 13 | 2.127 | 0.155 | 0.305 | 3.00 | NO | 0.538 | 0.429 | ||||
| Experimental | vocab.teach.pre | 230 | 4.796 | 4.5 | 1 | 10 | 1.861 | 0.123 | 0.242 | 2.00 | YES | 0.354 | -0.235 | |||||
| Controle | Rural | vocab.teach.pos | 253 | 5.016 | 5.0 | 0 | 15 | 2.384 | 0.150 | 0.295 | 4.00 | YES | 0.372 | 0.263 | ||||
| Controle | Urbana | vocab.teach.pos | 114 | 4.833 | 5.0 | 0 | 10 | 2.052 | 0.192 | 0.381 | 2.00 | YES | 0.125 | -0.173 | ||||
| Controle | vocab.teach.pos | 149 | 4.819 | 5.0 | 0 | 11 | 2.322 | 0.190 | 0.376 | 3.00 | YES | 0.206 | -0.328 | |||||
| Experimental | Rural | vocab.teach.pos | 294 | 5.374 | 5.0 | 0 | 12 | 2.358 | 0.138 | 0.271 | 3.00 | YES | 0.168 | -0.455 | ||||
| Experimental | Urbana | vocab.teach.pos | 189 | 5.487 | 6.0 | 1 | 11 | 2.163 | 0.157 | 0.310 | 3.00 | YES | 0.225 | -0.404 | ||||
| Experimental | vocab.teach.pos | 230 | 5.365 | 5.0 | 1 | 12 | 2.258 | 0.149 | 0.293 | 3.00 | YES | 0.256 | -0.363 | |||||
| Controle | Parda | vocab.teach.pre | 168 | 4.458 | 4.0 | 0 | 13 | 2.166 | 0.167 | 0.330 | 3.00 | NO | 0.546 | 0.619 | ||||
| Controle | Indígena | vocab.teach.pre | 13 | 4.615 | 4.0 | 1 | 7 | 1.660 | 0.460 | 1.003 | 2.00 | YES | -0.338 | -0.464 | ||||
| Controle | Branca | vocab.teach.pre | 54 | 4.352 | 4.0 | 1 | 11 | 2.267 | 0.308 | 0.619 | 3.75 | NO | 0.590 | -0.056 | ||||
| Controle | Preta | vocab.teach.pre | 1 | 3.000 | 3.0 | 3 | 3 | 0.00 | few data | 0.000 | 0.000 | |||||||
| Controle | vocab.teach.pre | 280 | 4.564 | 4.0 | 0 | 10 | 2.173 | 0.130 | 0.256 | 3.00 | YES | 0.204 | -0.672 | |||||
| Experimental | Parda | vocab.teach.pre | 201 | 4.478 | 4.0 | 0 | 10 | 2.057 | 0.145 | 0.286 | 3.00 | YES | 0.454 | -0.356 | ||||
| Experimental | Indígena | vocab.teach.pre | 18 | 4.167 | 4.0 | 1 | 7 | 1.654 | 0.390 | 0.822 | 1.75 | YES | -0.250 | -1.004 | ||||
| Experimental | Branca | vocab.teach.pre | 62 | 5.000 | 5.0 | 1 | 8 | 1.765 | 0.224 | 0.448 | 2.00 | YES | -0.123 | -0.789 | ||||
| Experimental | Preta | vocab.teach.pre | 1 | 1.000 | 1.0 | 1 | 1 | 0.00 | few data | 0.000 | 0.000 | |||||||
| Experimental | Amarela | vocab.teach.pre | 1 | 3.000 | 3.0 | 3 | 3 | 0.00 | few data | 0.000 | 0.000 | |||||||
| Experimental | vocab.teach.pre | 430 | 4.893 | 5.0 | 1 | 13 | 2.028 | 0.098 | 0.192 | 2.75 | YES | 0.424 | 0.186 | |||||
| Controle | Parda | vocab.teach.pos | 168 | 5.006 | 5.0 | 0 | 10 | 2.265 | 0.175 | 0.345 | 2.50 | YES | 0.079 | -0.377 | ||||
| Controle | Indígena | vocab.teach.pos | 13 | 5.923 | 6.0 | 2 | 9 | 2.100 | 0.582 | 1.269 | 3.00 | YES | -0.305 | -1.134 | ||||
| Controle | Branca | vocab.teach.pos | 54 | 4.889 | 5.0 | 1 | 11 | 2.408 | 0.328 | 0.657 | 4.00 | YES | 0.295 | -0.654 | ||||
| Controle | Preta | vocab.teach.pos | 1 | 6.000 | 6.0 | 6 | 6 | 0.00 | few data | 0.000 | 0.000 | |||||||
| Controle | vocab.teach.pos | 280 | 4.821 | 5.0 | 0 | 15 | 2.299 | 0.137 | 0.270 | 3.00 | YES | 0.458 | 0.620 | |||||
| Experimental | Parda | vocab.teach.pos | 201 | 5.318 | 5.0 | 0 | 12 | 2.364 | 0.167 | 0.329 | 3.00 | YES | 0.381 | -0.144 | ||||
| Experimental | Indígena | vocab.teach.pos | 18 | 5.111 | 5.0 | 2 | 9 | 2.026 | 0.478 | 1.007 | 3.50 | YES | 0.098 | -1.067 | ||||
| Experimental | Branca | vocab.teach.pos | 62 | 5.387 | 6.0 | 1 | 10 | 2.335 | 0.297 | 0.593 | 4.00 | YES | -0.030 | -1.012 | ||||
| Experimental | Preta | vocab.teach.pos | 1 | 4.000 | 4.0 | 4 | 4 | 0.00 | few data | 0.000 | 0.000 | |||||||
| Experimental | Amarela | vocab.teach.pos | 1 | 6.000 | 6.0 | 6 | 6 | 0.00 | few data | 0.000 | 0.000 | |||||||
| Experimental | vocab.teach.pos | 430 | 5.456 | 5.0 | 0 | 11 | 2.239 | 0.108 | 0.212 | 3.00 | YES | 0.150 | -0.452 | |||||
| Controle | 6 ano | vocab.teach.pre | 146 | 3.925 | 4.0 | 0 | 10 | 1.901 | 0.157 | 0.311 | 3.00 | YES | 0.418 | -0.039 | ||||
| Controle | 7 ano | vocab.teach.pre | 152 | 4.013 | 4.0 | 1 | 11 | 1.919 | 0.156 | 0.308 | 2.00 | NO | 0.685 | 0.547 | ||||
| Controle | 8 ano | vocab.teach.pre | 100 | 4.560 | 4.5 | 0 | 10 | 2.298 | 0.230 | 0.456 | 3.00 | YES | 0.050 | -0.769 | ||||
| Controle | 9 ano | vocab.teach.pre | 118 | 5.814 | 6.0 | 1 | 13 | 2.108 | 0.194 | 0.384 | 3.00 | YES | 0.043 | 0.248 | ||||
| Experimental | 6 ano | vocab.teach.pre | 165 | 4.255 | 4.0 | 1 | 11 | 1.902 | 0.148 | 0.292 | 2.00 | NO | 0.618 | 0.553 | ||||
| Experimental | 7 ano | vocab.teach.pre | 196 | 4.745 | 5.0 | 1 | 13 | 2.157 | 0.154 | 0.304 | 3.00 | NO | 0.549 | 0.323 | ||||
| Experimental | 8 ano | vocab.teach.pre | 181 | 4.796 | 5.0 | 1 | 9 | 1.954 | 0.145 | 0.287 | 3.00 | YES | 0.175 | -0.643 | ||||
| Experimental | 9 ano | vocab.teach.pre | 171 | 5.222 | 5.0 | 0 | 11 | 1.927 | 0.147 | 0.291 | 3.00 | YES | 0.171 | -0.238 | ||||
| Controle | 6 ano | vocab.teach.pos | 146 | 4.158 | 4.0 | 0 | 9 | 1.957 | 0.162 | 0.320 | 3.00 | YES | 0.170 | -0.451 | ||||
| Controle | 7 ano | vocab.teach.pos | 152 | 4.658 | 5.0 | 0 | 10 | 2.040 | 0.165 | 0.327 | 3.00 | YES | 0.287 | -0.267 | ||||
| Controle | 8 ano | vocab.teach.pos | 100 | 5.070 | 5.0 | 0 | 10 | 2.508 | 0.251 | 0.498 | 4.00 | YES | -0.166 | -0.786 | ||||
| Controle | 9 ano | vocab.teach.pos | 118 | 6.068 | 6.0 | 1 | 15 | 2.360 | 0.217 | 0.430 | 2.75 | YES | 0.427 | 0.657 | ||||
| Experimental | 6 ano | vocab.teach.pos | 165 | 5.164 | 5.0 | 0 | 11 | 2.346 | 0.183 | 0.361 | 4.00 | YES | 0.244 | -0.620 | ||||
| Experimental | 7 ano | vocab.teach.pos | 196 | 4.980 | 5.0 | 0 | 11 | 2.134 | 0.152 | 0.301 | 3.00 | YES | 0.405 | -0.068 | ||||
| Experimental | 8 ano | vocab.teach.pos | 181 | 5.210 | 5.0 | 1 | 11 | 2.188 | 0.163 | 0.321 | 3.00 | YES | 0.356 | -0.343 | ||||
| Experimental | 9 ano | vocab.teach.pos | 171 | 6.316 | 6.0 | 0 | 12 | 2.211 | 0.169 | 0.334 | 3.00 | YES | -0.202 | 0.117 | ||||
| Controle | 1st quintile | vocab.teach.pre | 102 | 1.618 | 2.0 | 0 | 2 | 0.598 | 0.059 | 0.117 | 1.00 | few data | 0.000 | 0.000 | ||||
| Controle | 2nd quintile | vocab.teach.pre | 80 | 3.000 | 3.0 | 3 | 3 | 0.000 | 0.000 | 0.000 | 0.00 | few data | 0.000 | 0.000 | ||||
| Controle | 3rd quintile | vocab.teach.pre | 173 | 4.509 | 5.0 | 4 | 5 | 0.501 | 0.038 | 0.075 | 1.00 | few data | 0.000 | 0.000 | ||||
| Controle | 4th quintile | vocab.teach.pre | 64 | 6.000 | 6.0 | 6 | 6 | 0.000 | 0.000 | 0.000 | 0.00 | few data | 0.000 | 0.000 | ||||
| Controle | 5th quintile | vocab.teach.pre | 97 | 7.794 | 7.0 | 7 | 13 | 1.060 | 0.108 | 0.214 | 1.00 | NO | 1.866 | 5.018 | ||||
| Experimental | 1st quintile | vocab.teach.pre | 93 | 1.710 | 2.0 | 0 | 2 | 0.480 | 0.050 | 0.099 | 1.00 | few data | 0.000 | 0.000 | ||||
| Experimental | 2nd quintile | vocab.teach.pre | 105 | 3.000 | 3.0 | 3 | 3 | 0.000 | 0.000 | 0.000 | 0.00 | few data | 0.000 | 0.000 | ||||
| Experimental | 3rd quintile | vocab.teach.pre | 273 | 4.451 | 4.0 | 4 | 5 | 0.498 | 0.030 | 0.059 | 1.00 | few data | 0.000 | 0.000 | ||||
| Experimental | 4th quintile | vocab.teach.pre | 99 | 6.000 | 6.0 | 6 | 6 | 0.000 | 0.000 | 0.000 | 0.00 | few data | 0.000 | 0.000 | ||||
| Experimental | 5th quintile | vocab.teach.pre | 143 | 7.762 | 7.0 | 7 | 13 | 1.034 | 0.086 | 0.171 | 1.00 | NO | 1.809 | 4.529 | ||||
| Controle | 1st quintile | vocab.teach.pos | 102 | 3.431 | 3.0 | 0 | 10 | 1.927 | 0.191 | 0.378 | 2.75 | YES | 0.412 | 0.170 | ||||
| Controle | 2nd quintile | vocab.teach.pos | 80 | 4.138 | 4.0 | 0 | 9 | 1.674 | 0.187 | 0.373 | 2.00 | YES | 0.265 | 0.205 | ||||
| Controle | 3rd quintile | vocab.teach.pos | 173 | 4.694 | 5.0 | 0 | 15 | 2.047 | 0.156 | 0.307 | 3.00 | NO | 0.746 | 2.761 | ||||
| Controle | 4th quintile | vocab.teach.pos | 64 | 5.922 | 6.0 | 0 | 10 | 2.125 | 0.266 | 0.531 | 2.25 | NO | -0.573 | -0.201 | ||||
| Controle | 5th quintile | vocab.teach.pos | 97 | 6.866 | 7.0 | 2 | 11 | 2.024 | 0.206 | 0.408 | 2.00 | YES | -0.245 | -0.452 | ||||
| Experimental | 1st quintile | vocab.teach.pos | 93 | 3.871 | 3.0 | 1 | 10 | 1.946 | 0.202 | 0.401 | 2.00 | NO | 0.617 | -0.028 | ||||
| Experimental | 2nd quintile | vocab.teach.pos | 105 | 4.686 | 5.0 | 1 | 9 | 1.723 | 0.168 | 0.333 | 3.00 | YES | 0.107 | -0.648 | ||||
| Experimental | 3rd quintile | vocab.teach.pos | 273 | 5.154 | 5.0 | 0 | 12 | 2.058 | 0.125 | 0.245 | 3.00 | YES | 0.228 | -0.010 | ||||
| Experimental | 4th quintile | vocab.teach.pos | 99 | 5.778 | 6.0 | 0 | 11 | 2.183 | 0.219 | 0.435 | 3.00 | YES | -0.155 | -0.146 | ||||
| Experimental | 5th quintile | vocab.teach.pos | 143 | 7.133 | 7.0 | 1 | 12 | 2.173 | 0.182 | 0.359 | 3.00 | YES | -0.276 | -0.418 |
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "vocab.teach.pos", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, vocab.teach.pos ~ grupo, covariate = vocab.teach.pre,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "vocab.teach.pos", "grupo", covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo"]] = wdat
(non.normal)
## [1] "P3709"
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 vocab.teach.pre 1 1225 380.801 4.64e-74 * 0.237
## 2 grupo 1 1225 10.282 1.00e-03 * 0.008
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| vocab.teach.pre | 1 | 1225 | 380.801 | 0.000 | * | 0.237 |
| grupo | 1 | 1225 | 10.282 | 0.001 | * | 0.008 |
pwc <- emmeans_test(wdat, vocab.teach.pos ~ grupo, covariate = vocab.teach.pre,
p.adjust.method = "bonferroni")
| term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|
| vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1225 | -3.207 | 0.001 | 0.001 | ** |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| grupo | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|
| Controle | time | vocab.teach | pre | pos | 2452 | -2.909 | 0.004 | 0.004 | ** |
| Experimental | time | vocab.teach | pre | pos | 2452 | -5.577 | 0.000 | 0.000 | **** |
ds <- get.descriptives(wdat, "vocab.teach.pos", "grupo", covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| grupo | N | M (pre) | SE (pre) | M (unadj) | SE (unadj) | M (adj) | SE (adj) | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|
| Controle | 515 | 4.505 | 0.095 | 4.899 | 0.099 | 4.977 | 0.087 | 4.806 | 5.148 |
| Experimental | 713 | 4.759 | 0.076 | 5.401 | 0.085 | 5.345 | 0.074 | 5.199 | 5.490 |
plots <- oneWayAncovaPlots(
wdat, "vocab.teach.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
plots <- oneWayAncovaBoxPlots(
wdat, "vocab.teach.pos", "grupo", aov, pwc, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "vocab.teach", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.75,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
## Warning: The dot-dot notation (`..eq.label..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(eq.label)` instead.
## ℹ The deprecated feature was likely used in the ggpubr package.
## Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo, data = wdat))
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.998 0.263
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 1226 2.40 0.121
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Sexo"]]),],
"vocab.teach.pos", c("grupo","Sexo"))
pdat = pdat[pdat[["Sexo"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Sexo"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Sexo"]] = factor(
pdat[["Sexo"]],
level[["Sexo"]][level[["Sexo"]] %in% unique(pdat[["Sexo"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Sexo")], pdat[,c("id","grupo","Sexo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo)
laov[["grupo:Sexo"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ Sexo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Sexo), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Sexo")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Sexo"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- ds
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Sexo")], wdat[,c("id","grupo","Sexo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:Sexo"]] = wdat
(non.normal)
}
## [1] "P3709"
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo)
laov[["grupo:Sexo"]] <- merge(get_anova_table(aov), laov[["grupo:Sexo"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| vocab.teach.pre | 1 | 1223 | 383.236 | 0.000 | * | 0.239 |
| grupo | 1 | 1223 | 10.087 | 0.002 | * | 0.008 |
| Sexo | 1 | 1223 | 13.356 | 0.000 | * | 0.011 |
| grupo:Sexo | 1 | 1223 | 3.710 | 0.054 | 0.003 |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ Sexo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Sexo), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
| grupo | Sexo | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| F | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1223 | -3.615 | 0.000 | 0.000 | *** | |
| M | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1223 | -0.859 | 0.391 | 0.391 | ns | |
| Controle | vocab.teach.pre*Sexo | vocab.teach.pos | F | M | 1223 | 0.900 | 0.368 | 0.368 | ns | |
| Experimental | vocab.teach.pre*Sexo | vocab.teach.pos | F | M | 1223 | 4.032 | 0.000 | 0.000 | **** |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Sexo")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Sexo"]],
by=c("grupo","Sexo","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| grupo | Sexo | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | F | time | vocab.teach | pre | pos | 2448 | -2.273 | 0.023 | 0.023 | * |
| Controle | M | time | vocab.teach | pre | pos | 2448 | -1.851 | 0.064 | 0.064 | ns |
| Experimental | F | time | vocab.teach | pre | pos | 2448 | -5.761 | 0.000 | 0.000 | **** |
| Experimental | M | time | vocab.teach | pre | pos | 2448 | -2.089 | 0.037 | 0.037 | * |
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Sexo"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- merge(ds, lemms[["grupo:Sexo"]],
by=c("grupo","Sexo"), suffixes = c("","'"))
}
| grupo | Sexo | N | M (pre) | SE (pre) | M (unadj) | SE (unadj) | M (adj) | SE (adj) | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | F | 258 | 4.585 | 0.127 | 5.019 | 0.138 | 5.055 | 0.122 | 4.815 | 5.295 |
| Controle | M | 257 | 4.424 | 0.143 | 4.778 | 0.142 | 4.899 | 0.123 | 4.658 | 5.140 |
| Experimental | F | 368 | 4.777 | 0.108 | 5.698 | 0.117 | 5.632 | 0.103 | 5.431 | 5.834 |
| Experimental | M | 345 | 4.739 | 0.106 | 5.084 | 0.122 | 5.038 | 0.106 | 4.830 | 5.246 |
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Sexo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "Sexo", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","Sexo"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:Sexo"]],
subtitle = which(aov$Effect == "grupo:Sexo"))
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
plots[["grupo:Sexo"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","Sexo"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Sexo"]])) >= 2)
plots[["grupo:Sexo"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","Sexo"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "Sexo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "Sexo", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Sexo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Sexo"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Sexo, data = wdat))
if (length(unique(pdat[["Sexo"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.999 0.459
if (length(unique(pdat[["Sexo"]])) >= 2)
levene_test(res, .resid ~ grupo*Sexo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 1224 0.660 0.577
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Zona"]]),],
"vocab.teach.pos", c("grupo","Zona"))
pdat = pdat[pdat[["Zona"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Zona"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Zona"]] = factor(
pdat[["Zona"]],
level[["Zona"]][level[["Zona"]] %in% unique(pdat[["Zona"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Zona")], pdat[,c("id","grupo","Zona")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Zona)
laov[["grupo:Zona"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ Zona,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Zona), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Zona")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Zona"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- ds
}
if (length(unique(pdat[["Zona"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Zona, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Zona")], wdat[,c("id","grupo","Zona")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:Zona"]] = wdat
(non.normal)
}
## [1] "P3709"
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Zona)
laov[["grupo:Zona"]] <- merge(get_anova_table(aov), laov[["grupo:Zona"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| vocab.teach.pre | 1 | 844 | 287.941 | 0.000 | * | 0.254 |
| grupo | 1 | 844 | 5.704 | 0.017 | * | 0.007 |
| Zona | 1 | 844 | 0.020 | 0.887 | 0.000 | |
| grupo:Zona | 1 | 844 | 0.323 | 0.570 | 0.000 |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ Zona,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Zona), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
| grupo | Zona | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| Rural | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 844 | -1.605 | 0.109 | 0.109 | ns | |
| Urbana | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 844 | -1.862 | 0.063 | 0.063 | ns | |
| Controle | vocab.teach.pre*Zona | vocab.teach.pos | Rural | Urbana | 844 | 0.528 | 0.598 | 0.598 | ns | |
| Experimental | vocab.teach.pre*Zona | vocab.teach.pos | Rural | Urbana | 844 | -0.253 | 0.801 | 0.801 | ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Zona")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Zona"]],
by=c("grupo","Zona","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| grupo | Zona | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | Rural | time | vocab.teach | pre | pos | 1690 | -2.672 | 0.008 | 0.008 | ** |
| Controle | Urbana | time | vocab.teach | pre | pos | 1690 | -1.467 | 0.143 | 0.143 | ns |
| Experimental | Rural | time | vocab.teach | pre | pos | 1690 | -3.806 | 0.000 | 0.000 | *** |
| Experimental | Urbana | time | vocab.teach | pre | pos | 1690 | -3.015 | 0.003 | 0.003 | ** |
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Zona"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- merge(ds, lemms[["grupo:Zona"]],
by=c("grupo","Zona"), suffixes = c("","'"))
}
| grupo | Zona | N | M (pre) | SE (pre) | M (unadj) | SE (unadj) | M (adj) | SE (adj) | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | Rural | 252 | 4.460 | 0.131 | 4.976 | 0.145 | 5.061 | 0.123 | 4.819 | 5.302 |
| Controle | Urbana | 114 | 4.412 | 0.184 | 4.833 | 0.192 | 4.944 | 0.183 | 4.585 | 5.303 |
| Experimental | Rural | 294 | 4.694 | 0.121 | 5.374 | 0.138 | 5.330 | 0.114 | 5.106 | 5.554 |
| Experimental | Urbana | 189 | 4.815 | 0.155 | 5.487 | 0.157 | 5.376 | 0.142 | 5.097 | 5.655 |
if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Zona", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "Zona", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","Zona"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:Zona"]],
subtitle = which(aov$Effect == "grupo:Zona"))
}
if (length(unique(pdat[["Zona"]])) >= 2) {
plots[["grupo:Zona"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","Zona"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Zona"]])) >= 2)
plots[["grupo:Zona"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","Zona"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "Zona", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "Zona", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Zona)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Zona"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Zona, data = wdat))
if (length(unique(pdat[["Zona"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.998 0.498
if (length(unique(pdat[["Zona"]])) >= 2)
levene_test(res, .resid ~ grupo*Zona)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 845 1.37 0.252
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Cor.Raca"]]),],
"vocab.teach.pos", c("grupo","Cor.Raca"))
## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.
pdat = pdat[pdat[["Cor.Raca"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Cor.Raca"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Cor.Raca"]] = factor(
pdat[["Cor.Raca"]],
level[["Cor.Raca"]][level[["Cor.Raca"]] %in% unique(pdat[["Cor.Raca"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Cor.Raca")], pdat[,c("id","grupo","Cor.Raca")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ Cor.Raca,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Cor.Raca), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Cor.Raca")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Cor.Raca"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- ds
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Cor.Raca")], wdat[,c("id","grupo","Cor.Raca")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:Cor.Raca"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- merge(get_anova_table(aov), laov[["grupo:Cor.Raca"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| vocab.teach.pre | 1 | 509 | 188.846 | 0.000 | * | 0.271 |
| grupo | 1 | 509 | 1.436 | 0.231 | 0.003 | |
| Cor.Raca | 2 | 509 | 0.773 | 0.462 | 0.003 | |
| grupo:Cor.Raca | 2 | 509 | 0.683 | 0.506 | 0.003 |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ Cor.Raca,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Cor.Raca), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
| grupo | Cor.Raca | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| Parda | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 509 | -1.455 | 0.146 | 0.146 | ns | |
| Indígena | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 509 | 0.763 | 0.446 | 0.446 | ns | |
| Branca | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 509 | -0.324 | 0.746 | 0.746 | ns | |
| Controle | vocab.teach.pre*Cor.Raca | vocab.teach.pos | Parda | Indígena | 509 | -1.448 | 0.148 | 0.445 | ns | |
| Controle | vocab.teach.pre*Cor.Raca | vocab.teach.pos | Parda | Branca | 509 | 0.177 | 0.860 | 1.000 | ns | |
| Controle | vocab.teach.pre*Cor.Raca | vocab.teach.pos | Indígena | Branca | 509 | 1.438 | 0.151 | 0.453 | ns | |
| Experimental | vocab.teach.pre*Cor.Raca | vocab.teach.pos | Parda | Indígena | 509 | 0.053 | 0.958 | 1.000 | ns | |
| Experimental | vocab.teach.pre*Cor.Raca | vocab.teach.pos | Parda | Branca | 509 | 0.819 | 0.413 | 1.000 | ns | |
| Experimental | vocab.teach.pre*Cor.Raca | vocab.teach.pos | Indígena | Branca | 509 | 0.396 | 0.692 | 1.000 | ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Cor.Raca")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| grupo | Cor.Raca | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | Parda | time | vocab.teach | pre | pos | 1020 | -2.288 | 0.022 | 0.022 | * |
| Controle | Indígena | time | vocab.teach | pre | pos | 1020 | -1.520 | 0.129 | 0.129 | ns |
| Controle | Branca | time | vocab.teach | pre | pos | 1020 | -1.272 | 0.204 | 0.204 | ns |
| Experimental | Parda | time | vocab.teach | pre | pos | 1020 | -3.842 | 0.000 | 0.000 | *** |
| Experimental | Indígena | time | vocab.teach | pre | pos | 1020 | -1.292 | 0.197 | 0.197 | ns |
| Experimental | Branca | time | vocab.teach | pre | pos | 1020 | -0.982 | 0.326 | 0.326 | ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Cor.Raca"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- merge(ds, lemms[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca"), suffixes = c("","'"))
}
| grupo | Cor.Raca | N | M (pre) | SE (pre) | M (unadj) | SE (unadj) | M (adj) | SE (adj) | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | Branca | 54 | 4.352 | 0.308 | 4.889 | 0.328 | 4.983 | 0.270 | 4.454 | 5.513 |
| Controle | Indígena | 13 | 4.615 | 0.460 | 5.923 | 0.582 | 5.864 | 0.549 | 4.784 | 6.943 |
| Controle | Parda | 168 | 4.458 | 0.167 | 5.006 | 0.175 | 5.038 | 0.153 | 4.738 | 5.338 |
| Experimental | Branca | 62 | 5.000 | 0.224 | 5.387 | 0.297 | 5.103 | 0.252 | 4.607 | 5.599 |
| Experimental | Indígena | 18 | 4.167 | 0.390 | 5.111 | 0.478 | 5.314 | 0.467 | 4.396 | 6.231 |
| Experimental | Parda | 201 | 4.478 | 0.145 | 5.318 | 0.167 | 5.339 | 0.140 | 5.065 | 5.614 |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Cor.Raca", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "Cor.Raca", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","Cor.Raca"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:Cor.Raca"]],
subtitle = which(aov$Effect == "grupo:Cor.Raca"))
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","Cor.Raca"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","Cor.Raca"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "Cor.Raca", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "Cor.Raca", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Cor.Raca)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Cor.Raca, data = wdat))
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.998 0.919
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
levene_test(res, .resid ~ grupo*Cor.Raca)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 5 510 0.703 0.621
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Serie"]]),],
"vocab.teach.pos", c("grupo","Serie"))
pdat = pdat[pdat[["Serie"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Serie"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Serie"]] = factor(
pdat[["Serie"]],
level[["Serie"]][level[["Serie"]] %in% unique(pdat[["Serie"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Serie")], pdat[,c("id","grupo","Serie")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Serie)
laov[["grupo:Serie"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ Serie,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Serie), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Serie")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","Serie"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- ds
}
if (length(unique(pdat[["Serie"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Serie, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Serie")], wdat[,c("id","grupo","Serie")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:Serie"]] = wdat
(non.normal)
}
## [1] "P3709"
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*Serie)
laov[["grupo:Serie"]] <- merge(get_anova_table(aov), laov[["grupo:Serie"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| vocab.teach.pre | 1 | 1219 | 325.734 | 0.000 | * | 0.211 |
| grupo | 1 | 1219 | 10.035 | 0.002 | * | 0.008 |
| Serie | 3 | 1219 | 11.129 | 0.000 | * | 0.027 |
| grupo:Serie | 3 | 1219 | 3.860 | 0.009 | * | 0.009 |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ Serie,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Serie), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
| grupo | Serie | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| 6 ano | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1219 | -3.797 | 0.000 | 0.000 | *** | |
| 7 ano | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1219 | 0.209 | 0.834 | 0.834 | ns | |
| 8 ano | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1219 | -0.091 | 0.927 | 0.927 | ns | |
| 9 ano | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1219 | -2.661 | 0.008 | 0.008 | ** | |
| Controle | vocab.teach.pre*Serie | vocab.teach.pos | 6 ano | 7 ano | 1219 | -2.021 | 0.044 | 0.261 | ns | |
| Controle | vocab.teach.pre*Serie | vocab.teach.pos | 6 ano | 8 ano | 1219 | -2.347 | 0.019 | 0.115 | ns | |
| Controle | vocab.teach.pre*Serie | vocab.teach.pos | 6 ano | 9 ano | 1219 | -3.582 | 0.000 | 0.002 | ** | |
| Controle | vocab.teach.pre*Serie | vocab.teach.pos | 7 ano | 8 ano | 1219 | -0.552 | 0.581 | 1.000 | ns | |
| Controle | vocab.teach.pre*Serie | vocab.teach.pos | 7 ano | 9 ano | 1219 | -1.756 | 0.079 | 0.476 | ns | |
| Controle | vocab.teach.pre*Serie | vocab.teach.pos | 8 ano | 9 ano | 1219 | -1.088 | 0.277 | 1.000 | ns | |
| Experimental | vocab.teach.pre*Serie | vocab.teach.pos | 6 ano | 7 ano | 1219 | 2.081 | 0.038 | 0.226 | ns | |
| Experimental | vocab.teach.pre*Serie | vocab.teach.pos | 6 ano | 8 ano | 1219 | 1.067 | 0.286 | 1.000 | ns | |
| Experimental | vocab.teach.pre*Serie | vocab.teach.pos | 6 ano | 9 ano | 1219 | -3.119 | 0.002 | 0.011 | * | |
| Experimental | vocab.teach.pre*Serie | vocab.teach.pos | 7 ano | 8 ano | 1219 | -1.021 | 0.307 | 1.000 | ns | |
| Experimental | vocab.teach.pre*Serie | vocab.teach.pos | 7 ano | 9 ano | 1219 | -5.373 | 0.000 | 0.000 | **** | |
| Experimental | vocab.teach.pre*Serie | vocab.teach.pos | 8 ano | 9 ano | 1219 | -4.290 | 0.000 | 0.000 | *** |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Serie")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Serie"]],
by=c("grupo","Serie","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| grupo | Serie | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | 6 ano | time | vocab.teach | pre | pos | 2440 | -0.946 | 0.344 | 0.344 | ns |
| Controle | 7 ano | time | vocab.teach | pre | pos | 2440 | -2.671 | 0.008 | 0.008 | ** |
| Controle | 8 ano | time | vocab.teach | pre | pos | 2440 | -1.714 | 0.087 | 0.087 | ns |
| Controle | 9 ano | time | vocab.teach | pre | pos | 2440 | -0.621 | 0.534 | 0.534 | ns |
| Experimental | 6 ano | time | vocab.teach | pre | pos | 2440 | -3.924 | 0.000 | 0.000 | **** |
| Experimental | 7 ano | time | vocab.teach | pre | pos | 2440 | -1.104 | 0.270 | 0.270 | ns |
| Experimental | 8 ano | time | vocab.teach | pre | pos | 2440 | -1.873 | 0.061 | 0.061 | ns |
| Experimental | 9 ano | time | vocab.teach | pre | pos | 2440 | -4.806 | 0.000 | 0.000 | **** |
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","Serie"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- merge(ds, lemms[["grupo:Serie"]],
by=c("grupo","Serie"), suffixes = c("","'"))
}
| grupo | Serie | N | M (pre) | SE (pre) | M (unadj) | SE (unadj) | M (adj) | SE (adj) | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | 6 ano | 146 | 3.925 | 0.157 | 4.158 | 0.162 | 4.521 | 0.162 | 4.203 | 4.840 |
| Controle | 7 ano | 152 | 4.013 | 0.156 | 4.658 | 0.165 | 4.978 | 0.159 | 4.666 | 5.289 |
| Controle | 8 ano | 100 | 4.560 | 0.230 | 5.070 | 0.251 | 5.116 | 0.195 | 4.734 | 5.498 |
| Controle | 9 ano | 117 | 5.821 | 0.196 | 5.991 | 0.205 | 5.407 | 0.183 | 5.048 | 5.766 |
| Experimental | 6 ano | 165 | 4.255 | 0.148 | 5.164 | 0.183 | 5.363 | 0.152 | 5.064 | 5.661 |
| Experimental | 7 ano | 196 | 4.745 | 0.154 | 4.980 | 0.152 | 4.933 | 0.139 | 4.660 | 5.206 |
| Experimental | 8 ano | 181 | 4.796 | 0.145 | 5.210 | 0.163 | 5.138 | 0.145 | 4.854 | 5.422 |
| Experimental | 9 ano | 171 | 5.222 | 0.147 | 6.316 | 0.169 | 6.031 | 0.150 | 5.737 | 6.325 |
if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Serie", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "Serie", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","Serie"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:Serie"]],
subtitle = which(aov$Effect == "grupo:Serie"))
}
if (length(unique(pdat[["Serie"]])) >= 2) {
plots[["grupo:Serie"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","Serie"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Serie"]])) >= 2)
plots[["grupo:Serie"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","Serie"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "Serie", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "Serie", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Serie)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["Serie"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*Serie, data = wdat))
if (length(unique(pdat[["Serie"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.998 0.364
if (length(unique(pdat[["Serie"]])) >= 2)
levene_test(res, .resid ~ grupo*Serie)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 1220 2.86 0.00583
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["vocab.teach.quintile"]]),],
"vocab.teach.pos", c("grupo","vocab.teach.quintile"))
pdat = pdat[pdat[["vocab.teach.quintile"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["vocab.teach.quintile"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["vocab.teach.quintile"]] = factor(
pdat[["vocab.teach.quintile"]],
level[["vocab.teach.quintile"]][level[["vocab.teach.quintile"]] %in% unique(pdat[["vocab.teach.quintile"]])])
pdat.long <- rbind(pdat[,c("id","grupo","vocab.teach.quintile")], pdat[,c("id","grupo","vocab.teach.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["vocab.teach"]] <- c(pdat[["vocab.teach.pre"]], pdat[["vocab.teach.pos"]])
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
aov = anova_test(pdat, vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile)
laov[["grupo:vocab.teach.quintile"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["vocab.teach.quintile"]] <- emmeans_test(
group_by(pdat, grupo), vocab.teach.pos ~ vocab.teach.quintile,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, vocab.teach.quintile), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["vocab.teach.quintile"]])
pwc <- pwc[,c("grupo","vocab.teach.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","vocab.teach.quintile")])]
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","vocab.teach.quintile")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:vocab.teach.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ds <- get.descriptives(pdat, "vocab.teach.pos", c("grupo","vocab.teach.quintile"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","vocab.teach.quintile"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","vocab.teach.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","vocab.teach.quintile","n","mean.vocab.teach.pre","se.vocab.teach.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","vocab.teach.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:vocab.teach.quintile"]] <- ds
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
wdat = pdat
res = residuals(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","vocab.teach.quintile")], wdat[,c("id","grupo","vocab.teach.quintile")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["vocab.teach"]] <- c(wdat[["vocab.teach.pre"]], wdat[["vocab.teach.pos"]])
ldat[["grupo:vocab.teach.quintile"]] = wdat
(non.normal)
}
## [1] "P3709"
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
aov = anova_test(wdat, vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile)
laov[["grupo:vocab.teach.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:vocab.teach.quintile"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| Effect | DFn | DFd | F | p | p<.05 | ges |
|---|---|---|---|---|---|---|
| vocab.teach.pre | 1 | 1217 | 20.967 | 0.000 | * | 0.017 |
| grupo | 1 | 1217 | 10.914 | 0.001 | * | 0.009 |
| vocab.teach.quintile | 4 | 1217 | 1.750 | 0.137 | 0.006 | |
| grupo:vocab.teach.quintile | 4 | 1217 | 0.986 | 0.414 | 0.003 |
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["vocab.teach.quintile"]] <- emmeans_test(
group_by(wdat, grupo), vocab.teach.pos ~ vocab.teach.quintile,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, vocab.teach.quintile), vocab.teach.pos ~ grupo,
covariate = vocab.teach.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["vocab.teach.quintile"]])
pwc <- pwc[,c("grupo","vocab.teach.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","vocab.teach.quintile")])]
}
| grupo | vocab.teach.quintile | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| 1st quintile | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1217 | -1.409 | 0.159 | 0.159 | ns | |
| 2nd quintile | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1217 | -1.869 | 0.062 | 0.062 | ns | |
| 3rd quintile | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1217 | -2.828 | 0.005 | 0.005 | ** | |
| 4th quintile | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1217 | 0.455 | 0.649 | 0.649 | ns | |
| 5th quintile | vocab.teach.pre*grupo | vocab.teach.pos | Controle | Experimental | 1217 | -1.080 | 0.280 | 0.280 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 1st quintile | 2nd quintile | 1217 | -0.313 | 0.754 | 1.000 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 1st quintile | 3rd quintile | 1217 | 0.166 | 0.868 | 1.000 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 1st quintile | 4th quintile | 1217 | -1.093 | 0.275 | 1.000 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 1st quintile | 5th quintile | 1217 | -1.120 | 0.263 | 1.000 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 2nd quintile | 3rd quintile | 1217 | 0.536 | 0.592 | 1.000 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 2nd quintile | 4th quintile | 1217 | -1.076 | 0.282 | 1.000 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 2nd quintile | 5th quintile | 1217 | -1.154 | 0.249 | 1.000 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 3rd quintile | 4th quintile | 1217 | -1.966 | 0.050 | 0.495 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 3rd quintile | 5th quintile | 1217 | -1.973 | 0.049 | 0.487 | ns | |
| Controle | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 4th quintile | 5th quintile | 1217 | -0.440 | 0.660 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 1st quintile | 2nd quintile | 1217 | -0.814 | 0.416 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 1st quintile | 3rd quintile | 1217 | -0.236 | 0.813 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 1st quintile | 4th quintile | 1217 | -0.059 | 0.953 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 1st quintile | 5th quintile | 1217 | -0.965 | 0.335 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 2nd quintile | 3rd quintile | 1217 | 0.627 | 0.531 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 2nd quintile | 4th quintile | 1217 | 0.554 | 0.580 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 2nd quintile | 5th quintile | 1217 | -0.697 | 0.486 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 3rd quintile | 4th quintile | 1217 | 0.197 | 0.844 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 3rd quintile | 5th quintile | 1217 | -1.407 | 0.160 | 1.000 | ns | |
| Experimental | vocab.teach.pre*vocab.teach.quintile | vocab.teach.pos | 4th quintile | 5th quintile | 1217 | -1.893 | 0.059 | 0.585 | ns |
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","vocab.teach.quintile")),
vocab.teach ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:vocab.teach.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:vocab.teach.quintile"]],
by=c("grupo","vocab.teach.quintile","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| grupo | vocab.teach.quintile | term | .y. | group1 | group2 | df | statistic | p | p.adj | p.adj.signif |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | 1st quintile | time | vocab.teach | pre | pos | 2436 | -8.813 | 0.000 | 0.000 | **** |
| Controle | 2nd quintile | time | vocab.teach | pre | pos | 2436 | -4.895 | 0.000 | 0.000 | **** |
| Controle | 3rd quintile | time | vocab.teach | pre | pos | 2436 | -0.807 | 0.420 | 0.420 | ns |
| Controle | 4th quintile | time | vocab.teach | pre | pos | 2436 | 0.301 | 0.764 | 0.764 | ns |
| Controle | 5th quintile | time | vocab.teach | pre | pos | 2436 | 4.396 | 0.000 | 0.000 | **** |
| Experimental | 1st quintile | time | vocab.teach | pre | pos | 2436 | -10.028 | 0.000 | 0.000 | **** |
| Experimental | 2nd quintile | time | vocab.teach | pre | pos | 2436 | -8.310 | 0.000 | 0.000 | **** |
| Experimental | 3rd quintile | time | vocab.teach | pre | pos | 2436 | -5.591 | 0.000 | 0.000 | **** |
| Experimental | 4th quintile | time | vocab.teach | pre | pos | 2436 | 1.064 | 0.288 | 0.288 | ns |
| Experimental | 5th quintile | time | vocab.teach | pre | pos | 2436 | 3.621 | 0.000 | 0.000 | *** |
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ds <- get.descriptives(wdat, "vocab.teach.pos", c("grupo","vocab.teach.quintile"), covar = "vocab.teach.pre")
ds <- merge(ds[ds$variable != "vocab.teach.pre",],
ds[ds$variable == "vocab.teach.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","vocab.teach.quintile"), all.x = T, suffixes = c("", ".vocab.teach.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","vocab.teach.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","vocab.teach.quintile","n","mean.vocab.teach.pre","se.vocab.teach.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","vocab.teach.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:vocab.teach.quintile"]] <- merge(ds, lemms[["grupo:vocab.teach.quintile"]],
by=c("grupo","vocab.teach.quintile"), suffixes = c("","'"))
}
| grupo | vocab.teach.quintile | N | M (pre) | SE (pre) | M (unadj) | SE (unadj) | M (adj) | SE (adj) | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|---|---|
| Controle | 1st quintile | 102 | 1.618 | 0.059 | 3.431 | 0.191 | 4.759 | 0.350 | 4.073 | 5.446 |
| Controle | 2nd quintile | 80 | 3.000 | 0.000 | 4.138 | 0.187 | 4.861 | 0.272 | 4.328 | 5.393 |
| Controle | 3rd quintile | 172 | 4.506 | 0.038 | 4.634 | 0.144 | 4.698 | 0.151 | 4.401 | 4.995 |
| Controle | 4th quintile | 64 | 6.000 | 0.000 | 5.922 | 0.266 | 5.332 | 0.279 | 4.786 | 5.879 |
| Controle | 5th quintile | 97 | 7.794 | 0.108 | 6.866 | 0.206 | 5.491 | 0.361 | 4.783 | 6.200 |
| Experimental | 1st quintile | 93 | 1.710 | 0.050 | 3.871 | 0.202 | 5.159 | 0.348 | 4.476 | 5.841 |
| Experimental | 2nd quintile | 105 | 3.000 | 0.000 | 4.686 | 0.168 | 5.409 | 0.249 | 4.920 | 5.898 |
| Experimental | 3rd quintile | 273 | 4.451 | 0.030 | 5.154 | 0.125 | 5.242 | 0.121 | 5.004 | 5.480 |
| Experimental | 4th quintile | 99 | 6.000 | 0.000 | 5.778 | 0.219 | 5.188 | 0.237 | 4.724 | 5.652 |
| Experimental | 5th quintile | 143 | 7.762 | 0.086 | 7.133 | 0.182 | 5.772 | 0.340 | 5.105 | 6.439 |
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "vocab.teach.quintile", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:vocab.teach.quintile"), addParam = "errorbar") +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
ggplot2::scale_color_manual(values = color[["vocab.teach.quintile"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "vocab.teach.quintile", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:vocab.teach.quintile"), addParam = "errorbar") +
ggplot2::ylab("Vocabulary (post-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "vocab.teach.pos", c("grupo","vocab.teach.quintile"), aov, pwcs, covar = "vocab.teach.pre",
theme = "classic", color = color[["grupo:vocab.teach.quintile"]],
subtitle = which(aov$Effect == "grupo:vocab.teach.quintile"))
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
plots[["grupo:vocab.teach.quintile"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "vocab.teach", c("grupo","vocab.teach.quintile"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2)
plots[["grupo:vocab.teach.quintile"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
facet.by = c("grupo","vocab.teach.quintile"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "grupo", facet.by = "vocab.teach.quintile", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:vocab.teach.quintile"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2) {
ggscatter(wdat, x = "vocab.teach.pre", y = "vocab.teach.pos", size = 0.5,
color = "vocab.teach.quintile", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = vocab.teach.quintile)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:vocab.teach.quintile"))) +
ggplot2::scale_color_manual(values = color[["vocab.teach.quintile"]]) +
ggplot2::ylab("Vocabulary (post-test)") +
ggplot2::xlab("Vocabulary (pre-test)") +
theme(axis.title = element_text(size = 14),
legend.text = element_text(size = 16),
plot.subtitle = element_text(size = 18)) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2)
res <- augment(lm(vocab.teach.pos ~ vocab.teach.pre + grupo*vocab.teach.quintile, data = wdat))
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.998 0.287
if (length(unique(pdat[["vocab.teach.quintile"]])) >= 2)
levene_test(res, .resid ~ grupo*vocab.teach.quintile)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 9 1218 1.43 0.171
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| grupo | Sexo | Zona | Cor.Raca | Serie | vocab.teach.quintile | variable | n | mean | median | min | max | sd | se | ci | iqr | symmetry | skewness | kurtosis |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Controle | vocab.teach.pre | 515 | 4.505 | 4.0 | 0 | 13 | 2.166 | 0.095 | 0.187 | 3.00 | YES | 0.359 | -0.160 | |||||
| Experimental | vocab.teach.pre | 713 | 4.759 | 5.0 | 0 | 13 | 2.018 | 0.076 | 0.148 | 3.00 | YES | 0.383 | -0.021 | |||||
| vocab.teach.pre | 1228 | 4.652 | 4.0 | 0 | 13 | 2.084 | 0.059 | 0.117 | 3.00 | YES | 0.358 | -0.075 | ||||||
| Controle | vocab.teach.pos | 515 | 4.899 | 5.0 | 0 | 11 | 2.253 | 0.099 | 0.195 | 3.00 | YES | 0.164 | -0.426 | |||||
| Experimental | vocab.teach.pos | 713 | 5.401 | 5.0 | 0 | 12 | 2.273 | 0.085 | 0.167 | 3.00 | YES | 0.206 | -0.390 | |||||
| vocab.teach.pos | 1228 | 5.191 | 5.0 | 0 | 12 | 2.277 | 0.065 | 0.127 | 3.00 | YES | 0.188 | -0.382 | ||||||
| Controle | F | vocab.teach.pre | 258 | 4.585 | 5.0 | 0 | 10 | 2.035 | 0.127 | 0.250 | 3.00 | YES | 0.141 | -0.540 | ||||
| Controle | M | vocab.teach.pre | 257 | 4.424 | 4.0 | 0 | 13 | 2.290 | 0.143 | 0.281 | 3.00 | NO | 0.531 | 0.058 | ||||
| Experimental | F | vocab.teach.pre | 368 | 4.777 | 5.0 | 0 | 13 | 2.063 | 0.108 | 0.211 | 3.00 | YES | 0.375 | 0.137 | ||||
| Experimental | M | vocab.teach.pre | 345 | 4.739 | 5.0 | 1 | 11 | 1.971 | 0.106 | 0.209 | 3.00 | YES | 0.387 | -0.257 | ||||
| Controle | F | vocab.teach.pos | 258 | 5.019 | 5.0 | 0 | 10 | 2.222 | 0.138 | 0.272 | 2.00 | YES | 0.082 | -0.294 | ||||
| Controle | M | vocab.teach.pos | 257 | 4.778 | 5.0 | 0 | 11 | 2.281 | 0.142 | 0.280 | 3.00 | YES | 0.249 | -0.536 | ||||
| Experimental | F | vocab.teach.pos | 368 | 5.698 | 6.0 | 0 | 12 | 2.249 | 0.117 | 0.230 | 3.00 | YES | 0.212 | -0.394 | ||||
| Experimental | M | vocab.teach.pos | 345 | 5.084 | 5.0 | 0 | 12 | 2.258 | 0.122 | 0.239 | 4.00 | YES | 0.214 | -0.426 | ||||
| Controle | Rural | vocab.teach.pre | 252 | 4.460 | 4.0 | 0 | 11 | 2.073 | 0.131 | 0.257 | 3.00 | YES | 0.326 | -0.396 | ||||
| Controle | Urbana | vocab.teach.pre | 114 | 4.412 | 4.0 | 0 | 10 | 1.968 | 0.184 | 0.365 | 2.75 | YES | 0.306 | -0.024 | ||||
| Experimental | Rural | vocab.teach.pre | 294 | 4.694 | 5.0 | 0 | 11 | 2.068 | 0.121 | 0.237 | 3.00 | YES | 0.289 | -0.366 | ||||
| Experimental | Urbana | vocab.teach.pre | 189 | 4.815 | 5.0 | 1 | 13 | 2.127 | 0.155 | 0.305 | 3.00 | NO | 0.538 | 0.429 | ||||
| Controle | Rural | vocab.teach.pos | 252 | 4.976 | 5.0 | 0 | 10 | 2.304 | 0.145 | 0.286 | 4.00 | YES | 0.142 | -0.635 | ||||
| Controle | Urbana | vocab.teach.pos | 114 | 4.833 | 5.0 | 0 | 10 | 2.052 | 0.192 | 0.381 | 2.00 | YES | 0.125 | -0.173 | ||||
| Experimental | Rural | vocab.teach.pos | 294 | 5.374 | 5.0 | 0 | 12 | 2.358 | 0.138 | 0.271 | 3.00 | YES | 0.168 | -0.455 | ||||
| Experimental | Urbana | vocab.teach.pos | 189 | 5.487 | 6.0 | 1 | 11 | 2.163 | 0.157 | 0.310 | 3.00 | YES | 0.225 | -0.404 | ||||
| Controle | Parda | vocab.teach.pre | 168 | 4.458 | 4.0 | 0 | 13 | 2.166 | 0.167 | 0.330 | 3.00 | NO | 0.546 | 0.619 | ||||
| Controle | Indígena | vocab.teach.pre | 13 | 4.615 | 4.0 | 1 | 7 | 1.660 | 0.460 | 1.003 | 2.00 | YES | -0.338 | -0.464 | ||||
| Controle | Branca | vocab.teach.pre | 54 | 4.352 | 4.0 | 1 | 11 | 2.267 | 0.308 | 0.619 | 3.75 | NO | 0.590 | -0.056 | ||||
| Experimental | Parda | vocab.teach.pre | 201 | 4.478 | 4.0 | 0 | 10 | 2.057 | 0.145 | 0.286 | 3.00 | YES | 0.454 | -0.356 | ||||
| Experimental | Indígena | vocab.teach.pre | 18 | 4.167 | 4.0 | 1 | 7 | 1.654 | 0.390 | 0.822 | 1.75 | YES | -0.250 | -1.004 | ||||
| Experimental | Branca | vocab.teach.pre | 62 | 5.000 | 5.0 | 1 | 8 | 1.765 | 0.224 | 0.448 | 2.00 | YES | -0.123 | -0.789 | ||||
| Controle | Parda | vocab.teach.pos | 168 | 5.006 | 5.0 | 0 | 10 | 2.265 | 0.175 | 0.345 | 2.50 | YES | 0.079 | -0.377 | ||||
| Controle | Indígena | vocab.teach.pos | 13 | 5.923 | 6.0 | 2 | 9 | 2.100 | 0.582 | 1.269 | 3.00 | YES | -0.305 | -1.134 | ||||
| Controle | Branca | vocab.teach.pos | 54 | 4.889 | 5.0 | 1 | 11 | 2.408 | 0.328 | 0.657 | 4.00 | YES | 0.295 | -0.654 | ||||
| Experimental | Parda | vocab.teach.pos | 201 | 5.318 | 5.0 | 0 | 12 | 2.364 | 0.167 | 0.329 | 3.00 | YES | 0.381 | -0.144 | ||||
| Experimental | Indígena | vocab.teach.pos | 18 | 5.111 | 5.0 | 2 | 9 | 2.026 | 0.478 | 1.007 | 3.50 | YES | 0.098 | -1.067 | ||||
| Experimental | Branca | vocab.teach.pos | 62 | 5.387 | 6.0 | 1 | 10 | 2.335 | 0.297 | 0.593 | 4.00 | YES | -0.030 | -1.012 | ||||
| Controle | 6 ano | vocab.teach.pre | 146 | 3.925 | 4.0 | 0 | 10 | 1.901 | 0.157 | 0.311 | 3.00 | YES | 0.418 | -0.039 | ||||
| Controle | 7 ano | vocab.teach.pre | 152 | 4.013 | 4.0 | 1 | 11 | 1.919 | 0.156 | 0.308 | 2.00 | NO | 0.685 | 0.547 | ||||
| Controle | 8 ano | vocab.teach.pre | 100 | 4.560 | 4.5 | 0 | 10 | 2.298 | 0.230 | 0.456 | 3.00 | YES | 0.050 | -0.769 | ||||
| Controle | 9 ano | vocab.teach.pre | 117 | 5.821 | 6.0 | 1 | 13 | 2.116 | 0.196 | 0.387 | 3.00 | YES | 0.034 | 0.227 | ||||
| Experimental | 6 ano | vocab.teach.pre | 165 | 4.255 | 4.0 | 1 | 11 | 1.902 | 0.148 | 0.292 | 2.00 | NO | 0.618 | 0.553 | ||||
| Experimental | 7 ano | vocab.teach.pre | 196 | 4.745 | 5.0 | 1 | 13 | 2.157 | 0.154 | 0.304 | 3.00 | NO | 0.549 | 0.323 | ||||
| Experimental | 8 ano | vocab.teach.pre | 181 | 4.796 | 5.0 | 1 | 9 | 1.954 | 0.145 | 0.287 | 3.00 | YES | 0.175 | -0.643 | ||||
| Experimental | 9 ano | vocab.teach.pre | 171 | 5.222 | 5.0 | 0 | 11 | 1.927 | 0.147 | 0.291 | 3.00 | YES | 0.171 | -0.238 | ||||
| Controle | 6 ano | vocab.teach.pos | 146 | 4.158 | 4.0 | 0 | 9 | 1.957 | 0.162 | 0.320 | 3.00 | YES | 0.170 | -0.451 | ||||
| Controle | 7 ano | vocab.teach.pos | 152 | 4.658 | 5.0 | 0 | 10 | 2.040 | 0.165 | 0.327 | 3.00 | YES | 0.287 | -0.267 | ||||
| Controle | 8 ano | vocab.teach.pos | 100 | 5.070 | 5.0 | 0 | 10 | 2.508 | 0.251 | 0.498 | 4.00 | YES | -0.166 | -0.786 | ||||
| Controle | 9 ano | vocab.teach.pos | 117 | 5.991 | 6.0 | 1 | 11 | 2.219 | 0.205 | 0.406 | 2.00 | YES | 0.062 | -0.525 | ||||
| Experimental | 6 ano | vocab.teach.pos | 165 | 5.164 | 5.0 | 0 | 11 | 2.346 | 0.183 | 0.361 | 4.00 | YES | 0.244 | -0.620 | ||||
| Experimental | 7 ano | vocab.teach.pos | 196 | 4.980 | 5.0 | 0 | 11 | 2.134 | 0.152 | 0.301 | 3.00 | YES | 0.405 | -0.068 | ||||
| Experimental | 8 ano | vocab.teach.pos | 181 | 5.210 | 5.0 | 1 | 11 | 2.188 | 0.163 | 0.321 | 3.00 | YES | 0.356 | -0.343 | ||||
| Experimental | 9 ano | vocab.teach.pos | 171 | 6.316 | 6.0 | 0 | 12 | 2.211 | 0.169 | 0.334 | 3.00 | YES | -0.202 | 0.117 | ||||
| Controle | 1st quintile | vocab.teach.pre | 102 | 1.618 | 2.0 | 0 | 2 | 0.598 | 0.059 | 0.117 | 1.00 | few data | 0.000 | 0.000 | ||||
| Controle | 2nd quintile | vocab.teach.pre | 80 | 3.000 | 3.0 | 3 | 3 | 0.000 | 0.000 | 0.000 | 0.00 | few data | 0.000 | 0.000 | ||||
| Controle | 3rd quintile | vocab.teach.pre | 172 | 4.506 | 5.0 | 4 | 5 | 0.501 | 0.038 | 0.075 | 1.00 | few data | 0.000 | 0.000 | ||||
| Controle | 4th quintile | vocab.teach.pre | 64 | 6.000 | 6.0 | 6 | 6 | 0.000 | 0.000 | 0.000 | 0.00 | few data | 0.000 | 0.000 | ||||
| Controle | 5th quintile | vocab.teach.pre | 97 | 7.794 | 7.0 | 7 | 13 | 1.060 | 0.108 | 0.214 | 1.00 | NO | 1.866 | 5.018 | ||||
| Experimental | 1st quintile | vocab.teach.pre | 93 | 1.710 | 2.0 | 0 | 2 | 0.480 | 0.050 | 0.099 | 1.00 | few data | 0.000 | 0.000 | ||||
| Experimental | 2nd quintile | vocab.teach.pre | 105 | 3.000 | 3.0 | 3 | 3 | 0.000 | 0.000 | 0.000 | 0.00 | few data | 0.000 | 0.000 | ||||
| Experimental | 3rd quintile | vocab.teach.pre | 273 | 4.451 | 4.0 | 4 | 5 | 0.498 | 0.030 | 0.059 | 1.00 | few data | 0.000 | 0.000 | ||||
| Experimental | 4th quintile | vocab.teach.pre | 99 | 6.000 | 6.0 | 6 | 6 | 0.000 | 0.000 | 0.000 | 0.00 | few data | 0.000 | 0.000 | ||||
| Experimental | 5th quintile | vocab.teach.pre | 143 | 7.762 | 7.0 | 7 | 13 | 1.034 | 0.086 | 0.171 | 1.00 | NO | 1.809 | 4.529 | ||||
| Controle | 1st quintile | vocab.teach.pos | 102 | 3.431 | 3.0 | 0 | 10 | 1.927 | 0.191 | 0.378 | 2.75 | YES | 0.412 | 0.170 | ||||
| Controle | 2nd quintile | vocab.teach.pos | 80 | 4.138 | 4.0 | 0 | 9 | 1.674 | 0.187 | 0.373 | 2.00 | YES | 0.265 | 0.205 | ||||
| Controle | 3rd quintile | vocab.teach.pos | 172 | 4.634 | 5.0 | 0 | 10 | 1.895 | 0.144 | 0.285 | 3.00 | YES | 0.104 | -0.188 | ||||
| Controle | 4th quintile | vocab.teach.pos | 64 | 5.922 | 6.0 | 0 | 10 | 2.125 | 0.266 | 0.531 | 2.25 | NO | -0.573 | -0.201 | ||||
| Controle | 5th quintile | vocab.teach.pos | 97 | 6.866 | 7.0 | 2 | 11 | 2.024 | 0.206 | 0.408 | 2.00 | YES | -0.245 | -0.452 | ||||
| Experimental | 1st quintile | vocab.teach.pos | 93 | 3.871 | 3.0 | 1 | 10 | 1.946 | 0.202 | 0.401 | 2.00 | NO | 0.617 | -0.028 | ||||
| Experimental | 2nd quintile | vocab.teach.pos | 105 | 4.686 | 5.0 | 1 | 9 | 1.723 | 0.168 | 0.333 | 3.00 | YES | 0.107 | -0.648 | ||||
| Experimental | 3rd quintile | vocab.teach.pos | 273 | 5.154 | 5.0 | 0 | 12 | 2.058 | 0.125 | 0.245 | 3.00 | YES | 0.228 | -0.010 | ||||
| Experimental | 4th quintile | vocab.teach.pos | 99 | 5.778 | 6.0 | 0 | 11 | 2.183 | 0.219 | 0.435 | 3.00 | YES | -0.155 | -0.146 | ||||
| Experimental | 5th quintile | vocab.teach.pos | 143 | 7.133 | 7.0 | 1 | 12 | 2.173 | 0.182 | 0.359 | 3.00 | YES | -0.276 | -0.418 |
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| Effect | DFn | DFd | F | p | p<.05 | ges | DFn’ | DFd’ | F’ | p’ | p<.05’ | ges’ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | grupo | 1 | 1225 | 10.282 | 0.001 | * | 0.008 | 1 | 1226 | 9.066 | 0.003 | * | 0.007 |
| 2 | vocab.teach.pre | 1 | 1225 | 380.801 | 0.000 | * | 0.237 | 1 | 1226 | 374.895 | 0.000 | * | 0.234 |
| 4 | grupo:Sexo | 1 | 1223 | 3.710 | 0.054 | 0.003 | 1 | 1224 | 3.045 | 0.081 | 0.002 | ||
| 5 | Sexo | 1 | 1223 | 13.356 | 0.000 | * | 0.011 | 1 | 1224 | 14.128 | 0.000 | * | 0.011 |
| 8 | grupo:Zona | 1 | 844 | 0.323 | 0.570 | 0.000 | 1 | 845 | 0.479 | 0.489 | 0.001 | ||
| 10 | Zona | 1 | 844 | 0.020 | 0.887 | 0.000 | 1 | 845 | 0.063 | 0.802 | 0.000 | ||
| 11 | Cor.Raca | 2 | 509 | 0.773 | 0.462 | 0.003 | 2 | 509 | 0.773 | 0.462 | 0.003 | ||
| 13 | grupo:Cor.Raca | 2 | 509 | 0.683 | 0.506 | 0.003 | 2 | 509 | 0.683 | 0.506 | 0.003 | ||
| 16 | grupo:Serie | 3 | 1219 | 3.860 | 0.009 | * | 0.009 | 3 | 1220 | 3.564 | 0.014 | * | 0.009 |
| 17 | Serie | 3 | 1219 | 11.129 | 0.000 | * | 0.027 | 3 | 1220 | 11.904 | 0.000 | * | 0.028 |
| 20 | grupo:vocab.teach.quintile | 4 | 1217 | 0.986 | 0.414 | 0.003 | 4 | 1218 | 0.853 | 0.491 | 0.003 | ||
| 22 | vocab.teach.quintile | 4 | 1217 | 1.750 | 0.137 | 0.006 | 4 | 1218 | 1.465 | 0.210 | 0.005 |
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| grupo | Sexo | Zona | Cor.Raca | Serie | vocab.teach.quintile | group1 | group2 | df | statistic | p | p.adj | p.adj.signif | df’ | statistic’ | p’ | p.adj’ | p.adj.signif’ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Controle | pre | pos | 2452 | -2.909 | 0.004 | 0.004 | ** | 2454 | -3.037 | 0.002 | 0.002 | ** | |||||
| Experimental | pre | pos | 2452 | -5.577 | 0.000 | 0.000 | **** | 2454 | -5.555 | 0.000 | 0.000 | **** | |||||
| Controle | Experimental | 1225 | -3.207 | 0.001 | 0.001 | ** | 1226 | -3.011 | 0.003 | 0.003 | ** | ||||||
| Controle | F | pre | pos | 2448 | -2.273 | 0.023 | 0.023 | * | 2450 | -2.462 | 0.014 | 0.014 | * | ||||
| Controle | M | pre | pos | 2448 | -1.851 | 0.064 | 0.064 | ns | 2450 | -1.843 | 0.065 | 0.065 | ns | ||||
| Controle | F | M | 1223 | 0.900 | 0.368 | 0.368 | ns | 1224 | 1.107 | 0.268 | 0.268 | ns | |||||
| Experimental | F | pre | pos | 2448 | -5.761 | 0.000 | 0.000 | **** | 2450 | -5.739 | 0.000 | 0.000 | **** | ||||
| Experimental | M | pre | pos | 2448 | -2.089 | 0.037 | 0.037 | * | 2450 | -2.081 | 0.038 | 0.038 | * | ||||
| Experimental | F | M | 1223 | 4.032 | 0.000 | 0.000 | **** | 1224 | 3.994 | 0.000 | 0.000 | **** | |||||
| F | Controle | Experimental | 1223 | -3.615 | 0.000 | 0.000 | *** | 1224 | -3.350 | 0.001 | 0.001 | *** | |||||
| M | Controle | Experimental | 1223 | -0.859 | 0.391 | 0.391 | ns | 1224 | -0.849 | 0.396 | 0.396 | ns | |||||
| Controle | Rural | Urbana | 844 | 0.528 | 0.598 | 0.598 | ns | 845 | 0.693 | 0.488 | 0.488 | ns | |||||
| Controle | Rural | pre | pos | 1690 | -2.672 | 0.008 | 0.008 | ** | 1692 | -2.856 | 0.004 | 0.004 | ** | ||||
| Controle | Urbana | pre | pos | 1690 | -1.467 | 0.143 | 0.143 | ns | 1692 | -1.459 | 0.145 | 0.145 | ns | ||||
| Experimental | Rural | Urbana | 844 | -0.253 | 0.801 | 0.801 | ns | 845 | -0.248 | 0.804 | 0.804 | ns | |||||
| Experimental | Rural | pre | pos | 1690 | -3.806 | 0.000 | 0.000 | *** | 1692 | -3.785 | 0.000 | 0.000 | *** | ||||
| Experimental | Urbana | pre | pos | 1690 | -3.015 | 0.003 | 0.003 | ** | 1692 | -2.997 | 0.003 | 0.003 | ** | ||||
| Rural | Controle | Experimental | 844 | -1.605 | 0.109 | 0.109 | ns | 845 | -1.357 | 0.175 | 0.175 | ns | |||||
| Urbana | Controle | Experimental | 844 | -1.862 | 0.063 | 0.063 | ns | 845 | -1.834 | 0.067 | 0.067 | ns | |||||
| Controle | Branca | pre | pos | 1020 | -1.272 | 0.204 | 0.204 | ns | 1020 | -1.272 | 0.204 | 0.204 | ns | ||||
| Controle | Indígena | pre | pos | 1020 | -1.520 | 0.129 | 0.129 | ns | 1020 | -1.520 | 0.129 | 0.129 | ns | ||||
| Controle | Indígena | Branca | 509 | 1.438 | 0.151 | 0.453 | ns | 509 | 1.438 | 0.151 | 0.453 | ns | |||||
| Controle | Parda | Branca | 509 | 0.177 | 0.860 | 1.000 | ns | 509 | 0.177 | 0.860 | 1.000 | ns | |||||
| Controle | Parda | Indígena | 509 | -1.448 | 0.148 | 0.445 | ns | 509 | -1.448 | 0.148 | 0.445 | ns | |||||
| Controle | Parda | pre | pos | 1020 | -2.288 | 0.022 | 0.022 | * | 1020 | -2.288 | 0.022 | 0.022 | * | ||||
| Experimental | Branca | pre | pos | 1020 | -0.982 | 0.326 | 0.326 | ns | 1020 | -0.982 | 0.326 | 0.326 | ns | ||||
| Experimental | Indígena | pre | pos | 1020 | -1.292 | 0.197 | 0.197 | ns | 1020 | -1.292 | 0.197 | 0.197 | ns | ||||
| Experimental | Indígena | Branca | 509 | 0.396 | 0.692 | 1.000 | ns | 509 | 0.396 | 0.692 | 1.000 | ns | |||||
| Experimental | Parda | Branca | 509 | 0.819 | 0.413 | 1.000 | ns | 509 | 0.819 | 0.413 | 1.000 | ns | |||||
| Experimental | Parda | Indígena | 509 | 0.053 | 0.958 | 1.000 | ns | 509 | 0.053 | 0.958 | 1.000 | ns | |||||
| Experimental | Parda | pre | pos | 1020 | -3.842 | 0.000 | 0.000 | *** | 1020 | -3.842 | 0.000 | 0.000 | *** | ||||
| Branca | Controle | Experimental | 509 | -0.324 | 0.746 | 0.746 | ns | 509 | -0.324 | 0.746 | 0.746 | ns | |||||
| Indígena | Controle | Experimental | 509 | 0.763 | 0.446 | 0.446 | ns | 509 | 0.763 | 0.446 | 0.446 | ns | |||||
| Parda | Controle | Experimental | 509 | -1.455 | 0.146 | 0.146 | ns | 509 | -1.455 | 0.146 | 0.146 | ns | |||||
| Controle | 6 ano | pre | pos | 2440 | -0.946 | 0.344 | 0.344 | ns | 2442 | -0.942 | 0.346 | 0.346 | ns | ||||
| Controle | 7 ano | pre | pos | 2440 | -2.671 | 0.008 | 0.008 | ** | 2442 | -2.662 | 0.008 | 0.008 | ** | ||||
| Controle | 8 ano | pre | pos | 2440 | -1.714 | 0.087 | 0.087 | ns | 2442 | -1.708 | 0.088 | 0.088 | ns | ||||
| Controle | 9 ano | pre | pos | 2440 | -0.621 | 0.534 | 0.534 | ns | 2442 | -0.925 | 0.355 | 0.355 | ns | ||||
| Controle | 6 ano | 7 ano | 1219 | -2.021 | 0.044 | 0.261 | ns | 1220 | -2.003 | 0.045 | 0.272 | ns | |||||
| Controle | 6 ano | 8 ano | 1219 | -2.347 | 0.019 | 0.115 | ns | 1220 | -2.329 | 0.020 | 0.120 | ns | |||||
| Controle | 6 ano | 9 ano | 1219 | -3.582 | 0.000 | 0.002 | ** | 1220 | -3.890 | 0.000 | 0.001 | *** | |||||
| Controle | 7 ano | 8 ano | 1219 | -0.552 | 0.581 | 1.000 | ns | 1220 | -0.550 | 0.582 | 1.000 | ns | |||||
| Controle | 7 ano | 9 ano | 1219 | -1.756 | 0.079 | 0.476 | ns | 1220 | -2.080 | 0.038 | 0.227 | ns | |||||
| Controle | 8 ano | 9 ano | 1219 | -1.088 | 0.277 | 1.000 | ns | 1220 | -1.384 | 0.167 | 1.000 | ns | |||||
| Experimental | 6 ano | pre | pos | 2440 | -3.924 | 0.000 | 0.000 | **** | 2442 | -3.911 | 0.000 | 0.000 | **** | ||||
| Experimental | 7 ano | pre | pos | 2440 | -1.104 | 0.270 | 0.270 | ns | 2442 | -1.101 | 0.271 | 0.271 | ns | ||||
| Experimental | 8 ano | pre | pos | 2440 | -1.873 | 0.061 | 0.061 | ns | 2442 | -1.867 | 0.062 | 0.062 | ns | ||||
| Experimental | 9 ano | pre | pos | 2440 | -4.806 | 0.000 | 0.000 | **** | 2442 | -4.790 | 0.000 | 0.000 | **** | ||||
| Experimental | 6 ano | 7 ano | 1219 | 2.081 | 0.038 | 0.226 | ns | 1220 | 2.059 | 0.040 | 0.238 | ns | |||||
| Experimental | 6 ano | 8 ano | 1219 | 1.067 | 0.286 | 1.000 | ns | 1220 | 1.053 | 0.292 | 1.000 | ns | |||||
| Experimental | 6 ano | 9 ano | 1219 | -3.119 | 0.002 | 0.011 | * | 1220 | -3.098 | 0.002 | 0.012 | * | |||||
| Experimental | 7 ano | 8 ano | 1219 | -1.021 | 0.307 | 1.000 | ns | 1220 | -1.012 | 0.312 | 1.000 | ns | |||||
| Experimental | 7 ano | 9 ano | 1219 | -5.373 | 0.000 | 0.000 | **** | 1220 | -5.329 | 0.000 | 0.000 | **** | |||||
| Experimental | 8 ano | 9 ano | 1219 | -4.290 | 0.000 | 0.000 | *** | 1220 | -4.254 | 0.000 | 0.000 | *** | |||||
| 6 ano | Controle | Experimental | 1219 | -3.797 | 0.000 | 0.000 | *** | 1220 | -3.766 | 0.000 | 0.000 | *** | |||||
| 7 ano | Controle | Experimental | 1219 | 0.209 | 0.834 | 0.834 | ns | 1220 | 0.202 | 0.840 | 0.840 | ns | |||||
| 8 ano | Controle | Experimental | 1219 | -0.091 | 0.927 | 0.927 | ns | 1220 | -0.092 | 0.927 | 0.927 | ns | |||||
| 9 ano | Controle | Experimental | 1219 | -2.661 | 0.008 | 0.008 | ** | 1220 | -2.302 | 0.022 | 0.022 | * | |||||
| Controle | 1st quintile | pre | pos | 2436 | -8.813 | 0.000 | 0.000 | **** | 2438 | -8.728 | 0.000 | 0.000 | **** | ||||
| Controle | 2nd quintile | pre | pos | 2436 | -4.895 | 0.000 | 0.000 | **** | 2438 | -4.848 | 0.000 | 0.000 | **** | ||||
| Controle | 3rd quintile | pre | pos | 2436 | -0.807 | 0.420 | 0.420 | ns | 2438 | -1.159 | 0.246 | 0.246 | ns | ||||
| Controle | 4th quintile | pre | pos | 2436 | 0.301 | 0.764 | 0.764 | ns | 2438 | 0.298 | 0.766 | 0.766 | ns | ||||
| Controle | 5th quintile | pre | pos | 2436 | 4.396 | 0.000 | 0.000 | **** | 2438 | 4.354 | 0.000 | 0.000 | **** | ||||
| Controle | 1st quintile | 2nd quintile | 1217 | -0.313 | 0.754 | 1.000 | ns | 1218 | -0.261 | 0.794 | 1.000 | ns | |||||
| Controle | 1st quintile | 3rd quintile | 1217 | 0.166 | 0.868 | 1.000 | ns | 1218 | 0.098 | 0.922 | 1.000 | ns | |||||
| Controle | 1st quintile | 4th quintile | 1217 | -1.093 | 0.275 | 1.000 | ns | 1218 | -0.986 | 0.325 | 1.000 | ns | |||||
| Controle | 1st quintile | 5th quintile | 1217 | -1.120 | 0.263 | 1.000 | ns | 1218 | -1.000 | 0.317 | 1.000 | ns | |||||
| Controle | 2nd quintile | 3rd quintile | 1217 | 0.536 | 0.592 | 1.000 | ns | 1218 | 0.397 | 0.692 | 1.000 | ns | |||||
| Controle | 2nd quintile | 4th quintile | 1217 | -1.076 | 0.282 | 1.000 | ns | 1218 | -0.986 | 0.324 | 1.000 | ns | |||||
| Controle | 2nd quintile | 5th quintile | 1217 | -1.154 | 0.249 | 1.000 | ns | 1218 | -1.041 | 0.298 | 1.000 | ns | |||||
| Controle | 3rd quintile | 4th quintile | 1217 | -1.966 | 0.050 | 0.495 | ns | 1218 | -1.714 | 0.087 | 0.867 | ns | |||||
| Controle | 3rd quintile | 5th quintile | 1217 | -1.973 | 0.049 | 0.487 | ns | 1218 | -1.716 | 0.086 | 0.863 | ns | |||||
| Controle | 4th quintile | 5th quintile | 1217 | -0.440 | 0.660 | 1.000 | ns | 1218 | -0.379 | 0.705 | 1.000 | ns | |||||
| Experimental | 1st quintile | pre | pos | 2436 | -10.028 | 0.000 | 0.000 | **** | 2438 | -9.931 | 0.000 | 0.000 | **** | ||||
| Experimental | 2nd quintile | pre | pos | 2436 | -8.310 | 0.000 | 0.000 | **** | 2438 | -8.230 | 0.000 | 0.000 | **** | ||||
| Experimental | 3rd quintile | pre | pos | 2436 | -5.591 | 0.000 | 0.000 | **** | 2438 | -5.537 | 0.000 | 0.000 | **** | ||||
| Experimental | 4th quintile | pre | pos | 2436 | 1.064 | 0.288 | 0.288 | ns | 2438 | 1.054 | 0.292 | 0.292 | ns | ||||
| Experimental | 5th quintile | pre | pos | 2436 | 3.621 | 0.000 | 0.000 | *** | 2438 | 3.586 | 0.000 | 0.000 | *** | ||||
| Experimental | 1st quintile | 2nd quintile | 1217 | -0.814 | 0.416 | 1.000 | ns | 1218 | -0.757 | 0.449 | 1.000 | ns | |||||
| Experimental | 1st quintile | 3rd quintile | 1217 | -0.236 | 0.813 | 1.000 | ns | 1218 | -0.144 | 0.885 | 1.000 | ns | |||||
| Experimental | 1st quintile | 4th quintile | 1217 | -0.059 | 0.953 | 1.000 | ns | 1218 | 0.041 | 0.967 | 1.000 | ns | |||||
| Experimental | 1st quintile | 5th quintile | 1217 | -0.965 | 0.335 | 1.000 | ns | 1218 | -0.846 | 0.398 | 1.000 | ns | |||||
| Experimental | 2nd quintile | 3rd quintile | 1217 | 0.627 | 0.531 | 1.000 | ns | 1218 | 0.683 | 0.495 | 1.000 | ns | |||||
| Experimental | 2nd quintile | 4th quintile | 1217 | 0.554 | 0.580 | 1.000 | ns | 1218 | 0.635 | 0.526 | 1.000 | ns | |||||
| Experimental | 2nd quintile | 5th quintile | 1217 | -0.697 | 0.486 | 1.000 | ns | 1218 | -0.585 | 0.559 | 1.000 | ns | |||||
| Experimental | 3rd quintile | 4th quintile | 1217 | 0.197 | 0.844 | 1.000 | ns | 1218 | 0.260 | 0.795 | 1.000 | ns | |||||
| Experimental | 3rd quintile | 5th quintile | 1217 | -1.407 | 0.160 | 1.000 | ns | 1218 | -1.292 | 0.197 | 1.000 | ns | |||||
| Experimental | 4th quintile | 5th quintile | 1217 | -1.893 | 0.059 | 0.585 | ns | 1218 | -1.808 | 0.071 | 0.708 | ns | |||||
| 1st quintile | Controle | Experimental | 1217 | -1.409 | 0.159 | 0.159 | ns | 1218 | -1.391 | 0.165 | 0.165 | ns | |||||
| 2nd quintile | Controle | Experimental | 1217 | -1.869 | 0.062 | 0.062 | ns | 1218 | -1.850 | 0.065 | 0.065 | ns | |||||
| 3rd quintile | Controle | Experimental | 1217 | -2.828 | 0.005 | 0.005 | ** | 1218 | -2.506 | 0.012 | 0.012 | * | |||||
| 4th quintile | Controle | Experimental | 1217 | 0.455 | 0.649 | 0.649 | ns | 1218 | 0.450 | 0.653 | 0.653 | ns | |||||
| 5th quintile | Controle | Experimental | 1217 | -1.080 | 0.280 | 0.280 | ns | 1218 | -1.070 | 0.285 | 0.285 | ns |
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| grupo | Sexo | Zona | Cor.Raca | Serie | vocab.teach.quintile | N | M (pre) | SE (pre) | M (unadj) | SE (unadj) | M (adj) | SE (adj) | conf.low | conf.high | N’ | M (pre)’ | SE (pre)’ | M (unadj)’ | SE (unadj)’ | M (adj)’ | SE (adj)’ | conf.low’ | conf.high’ | N-N’ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Controle | 515 | 4.505 | 0.095 | 4.899 | 0.099 | 4.977 | 0.087 | 4.806 | 5.148 | 516 | 4.506 | 0.095 | 4.919 | 0.101 | 4.996 | 0.088 | 4.824 | 5.169 | -1 | |||||
| Experimental | 713 | 4.759 | 0.076 | 5.401 | 0.085 | 5.345 | 0.074 | 5.199 | 5.490 | 713 | 4.759 | 0.076 | 5.401 | 0.085 | 5.345 | 0.075 | 5.198 | 5.492 | 0 | |||||
| Controle | F | 258 | 4.585 | 0.127 | 5.019 | 0.138 | 5.055 | 0.122 | 4.815 | 5.295 | 259 | 4.587 | 0.126 | 5.058 | 0.143 | 5.093 | 0.123 | 4.851 | 5.335 | -1 | ||||
| Controle | M | 257 | 4.424 | 0.143 | 4.778 | 0.142 | 4.899 | 0.123 | 4.658 | 5.140 | 257 | 4.424 | 0.143 | 4.778 | 0.142 | 4.899 | 0.124 | 4.656 | 5.142 | 0 | ||||
| Experimental | F | 368 | 4.777 | 0.108 | 5.698 | 0.117 | 5.632 | 0.103 | 5.431 | 5.834 | 368 | 4.777 | 0.108 | 5.698 | 0.117 | 5.632 | 0.104 | 5.429 | 5.836 | 0 | ||||
| Experimental | M | 345 | 4.739 | 0.106 | 5.084 | 0.122 | 5.038 | 0.106 | 4.830 | 5.246 | 345 | 4.739 | 0.106 | 5.084 | 0.122 | 5.038 | 0.107 | 4.828 | 5.248 | 0 | ||||
| Controle | Rural | 252 | 4.460 | 0.131 | 4.976 | 0.145 | 5.061 | 0.123 | 4.819 | 5.302 | 253 | 4.462 | 0.130 | 5.016 | 0.150 | 5.100 | 0.125 | 4.855 | 5.344 | -1 | ||||
| Controle | Urbana | 114 | 4.412 | 0.184 | 4.833 | 0.192 | 4.944 | 0.183 | 4.585 | 5.303 | 114 | 4.412 | 0.184 | 4.833 | 0.192 | 4.945 | 0.186 | 4.581 | 5.309 | 0 | ||||
| Experimental | Rural | 294 | 4.694 | 0.121 | 5.374 | 0.138 | 5.330 | 0.114 | 5.106 | 5.554 | 294 | 4.694 | 0.121 | 5.374 | 0.138 | 5.330 | 0.115 | 5.103 | 5.557 | 0 | ||||
| Experimental | Urbana | 189 | 4.815 | 0.155 | 5.487 | 0.157 | 5.376 | 0.142 | 5.097 | 5.655 | 189 | 4.815 | 0.155 | 5.487 | 0.157 | 5.376 | 0.144 | 5.093 | 5.659 | 0 | ||||
| Controle | Branca | 54 | 4.352 | 0.308 | 4.889 | 0.328 | 4.983 | 0.270 | 4.454 | 5.513 | 54 | 4.352 | 0.308 | 4.889 | 0.328 | 4.983 | 0.270 | 4.454 | 5.513 | 0 | ||||
| Controle | Indígena | 13 | 4.615 | 0.460 | 5.923 | 0.582 | 5.864 | 0.549 | 4.784 | 6.943 | 13 | 4.615 | 0.460 | 5.923 | 0.582 | 5.864 | 0.549 | 4.784 | 6.943 | 0 | ||||
| Controle | Parda | 168 | 4.458 | 0.167 | 5.006 | 0.175 | 5.038 | 0.153 | 4.738 | 5.338 | 168 | 4.458 | 0.167 | 5.006 | 0.175 | 5.038 | 0.153 | 4.738 | 5.338 | 0 | ||||
| Experimental | Branca | 62 | 5.000 | 0.224 | 5.387 | 0.297 | 5.103 | 0.252 | 4.607 | 5.599 | 62 | 5.000 | 0.224 | 5.387 | 0.297 | 5.103 | 0.252 | 4.607 | 5.599 | 0 | ||||
| Experimental | Indígena | 18 | 4.167 | 0.390 | 5.111 | 0.478 | 5.314 | 0.467 | 4.396 | 6.231 | 18 | 4.167 | 0.390 | 5.111 | 0.478 | 5.314 | 0.467 | 4.396 | 6.231 | 0 | ||||
| Experimental | Parda | 201 | 4.478 | 0.145 | 5.318 | 0.167 | 5.339 | 0.140 | 5.065 | 5.614 | 201 | 4.478 | 0.145 | 5.318 | 0.167 | 5.339 | 0.140 | 5.065 | 5.614 | 0 | ||||
| Controle | 6 ano | 146 | 3.925 | 0.157 | 4.158 | 0.162 | 4.521 | 0.162 | 4.203 | 4.840 | 146 | 3.925 | 0.157 | 4.158 | 0.162 | 4.520 | 0.164 | 4.199 | 4.842 | 0 | ||||
| Controle | 7 ano | 152 | 4.013 | 0.156 | 4.658 | 0.165 | 4.978 | 0.159 | 4.666 | 5.289 | 152 | 4.013 | 0.156 | 4.658 | 0.165 | 4.977 | 0.160 | 4.662 | 5.291 | 0 | ||||
| Controle | 8 ano | 100 | 4.560 | 0.230 | 5.070 | 0.251 | 5.116 | 0.195 | 4.734 | 5.498 | 100 | 4.560 | 0.230 | 5.070 | 0.251 | 5.116 | 0.197 | 4.731 | 5.502 | 0 | ||||
| Controle | 9 ano | 117 | 5.821 | 0.196 | 5.991 | 0.205 | 5.407 | 0.183 | 5.048 | 5.766 | 118 | 5.814 | 0.194 | 6.068 | 0.217 | 5.489 | 0.184 | 5.128 | 5.850 | -1 | ||||
| Experimental | 6 ano | 165 | 4.255 | 0.148 | 5.164 | 0.183 | 5.363 | 0.152 | 5.064 | 5.661 | 165 | 4.255 | 0.148 | 5.164 | 0.183 | 5.362 | 0.153 | 5.061 | 5.663 | 0 | ||||
| Experimental | 7 ano | 196 | 4.745 | 0.154 | 4.980 | 0.152 | 4.933 | 0.139 | 4.660 | 5.206 | 196 | 4.745 | 0.154 | 4.980 | 0.152 | 4.934 | 0.140 | 4.658 | 5.209 | 0 | ||||
| Experimental | 8 ano | 181 | 4.796 | 0.145 | 5.210 | 0.163 | 5.138 | 0.145 | 4.854 | 5.422 | 181 | 4.796 | 0.145 | 5.210 | 0.163 | 5.139 | 0.146 | 4.852 | 5.425 | 0 | ||||
| Experimental | 9 ano | 171 | 5.222 | 0.147 | 6.316 | 0.169 | 6.031 | 0.150 | 5.737 | 6.325 | 171 | 5.222 | 0.147 | 6.316 | 0.169 | 6.032 | 0.151 | 5.735 | 6.328 | 0 | ||||
| Controle | 1st quintile | 102 | 1.618 | 0.059 | 3.431 | 0.191 | 4.759 | 0.350 | 4.073 | 5.446 | 102 | 1.618 | 0.059 | 3.431 | 0.191 | 4.795 | 0.353 | 4.101 | 5.488 | 0 | ||||
| Controle | 2nd quintile | 80 | 3.000 | 0.000 | 4.138 | 0.187 | 4.861 | 0.272 | 4.328 | 5.393 | 80 | 3.000 | 0.000 | 4.138 | 0.187 | 4.880 | 0.274 | 4.342 | 5.418 | 0 | ||||
| Controle | 3rd quintile | 172 | 4.506 | 0.038 | 4.634 | 0.144 | 4.698 | 0.151 | 4.401 | 4.995 | 173 | 4.509 | 0.038 | 4.694 | 0.156 | 4.758 | 0.152 | 4.459 | 5.057 | -1 | ||||
| Controle | 4th quintile | 64 | 6.000 | 0.000 | 5.922 | 0.266 | 5.332 | 0.279 | 4.786 | 5.879 | 64 | 6.000 | 0.000 | 5.922 | 0.266 | 5.317 | 0.281 | 4.764 | 5.869 | 0 | ||||
| Controle | 5th quintile | 97 | 7.794 | 0.108 | 6.866 | 0.206 | 5.491 | 0.361 | 4.783 | 6.200 | 97 | 7.794 | 0.108 | 6.866 | 0.206 | 5.455 | 0.365 | 4.739 | 6.170 | 0 | ||||
| Experimental | 1st quintile | 93 | 1.710 | 0.050 | 3.871 | 0.202 | 5.159 | 0.348 | 4.476 | 5.841 | 93 | 1.710 | 0.050 | 3.871 | 0.202 | 5.193 | 0.351 | 4.503 | 5.883 | 0 | ||||
| Experimental | 2nd quintile | 105 | 3.000 | 0.000 | 4.686 | 0.168 | 5.409 | 0.249 | 4.920 | 5.898 | 105 | 3.000 | 0.000 | 4.686 | 0.168 | 5.428 | 0.252 | 4.934 | 5.922 | 0 | ||||
| Experimental | 3rd quintile | 273 | 4.451 | 0.030 | 5.154 | 0.125 | 5.242 | 0.121 | 5.004 | 5.480 | 273 | 4.451 | 0.030 | 5.154 | 0.125 | 5.245 | 0.122 | 5.004 | 5.485 | 0 | ||||
| Experimental | 4th quintile | 99 | 6.000 | 0.000 | 5.778 | 0.219 | 5.188 | 0.237 | 4.724 | 5.652 | 99 | 6.000 | 0.000 | 5.778 | 0.219 | 5.172 | 0.239 | 4.703 | 5.642 | 0 | ||||
| Experimental | 5th quintile | 143 | 7.762 | 0.086 | 7.133 | 0.182 | 5.772 | 0.340 | 5.105 | 6.439 | 143 | 7.762 | 0.086 | 7.133 | 0.182 | 5.736 | 0.343 | 5.062 | 6.410 | 0 |
df <- do.call(rbind, lapply(c("Sexo","Zona","Cor.Raca","Serie","Idade"), FUN = function(x) {
tdat2 <- tdat
tdat2[[x]][is.na(tdat2[[x]])] <- "Not declared"
count_(group_by(tdat2, grupo), x)
}))
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
| grupo | Sexo | n | Zona | Cor.Raca | Serie | Idade |
|---|---|---|---|---|---|---|
| Controle | F | 376 | ||||
| Controle | M | 393 | ||||
| Experimental | F | 508 | ||||
| Experimental | M | 528 | ||||
| Controle | 211 | Not declared | ||||
| Controle | 351 | Rural | ||||
| Controle | 207 | Urbana | ||||
| Experimental | 321 | Not declared | ||||
| Experimental | 426 | Rural | ||||
| Experimental | 289 | Urbana | ||||
| Controle | 77 | Branca | ||||
| Controle | 21 | Indígena | ||||
| Controle | 416 | Not declared | ||||
| Controle | 254 | Parda | ||||
| Controle | 1 | Preta | ||||
| Experimental | 3 | Amarela | ||||
| Experimental | 92 | Branca | ||||
| Experimental | 22 | Indígena | ||||
| Experimental | 619 | Not declared | ||||
| Experimental | 299 | Parda | ||||
| Experimental | 1 | Preta | ||||
| Controle | 215 | 6 ano | ||||
| Controle | 214 | 7 ano | ||||
| Controle | 163 | 8 ano | ||||
| Controle | 177 | 9 ano | ||||
| Experimental | 250 | 6 ano | ||||
| Experimental | 283 | 7 ano | ||||
| Experimental | 258 | 8 ano | ||||
| Experimental | 245 | 9 ano | ||||
| Controle | 112 | 11 | ||||
| Controle | 162 | 12 | ||||
| Controle | 183 | 13 | ||||
| Controle | 172 | 14 | ||||
| Controle | 90 | 15 | ||||
| Controle | 30 | 16 | ||||
| Controle | 9 | 17 | ||||
| Controle | 5 | 18 | ||||
| Controle | 1 | 19 | ||||
| Controle | 2 | 20 | ||||
| Controle | 1 | 21 | ||||
| Controle | 1 | 22 | ||||
| Controle | 1 | 34 | ||||
| Experimental | 1 | 1 | ||||
| Experimental | 160 | 11 | ||||
| Experimental | 215 | 12 | ||||
| Experimental | 226 | 13 | ||||
| Experimental | 220 | 14 | ||||
| Experimental | 126 | 15 | ||||
| Experimental | 52 | 16 | ||||
| Experimental | 21 | 17 | ||||
| Experimental | 7 | 18 | ||||
| Experimental | 3 | 19 | ||||
| Experimental | 1 | 20 | ||||
| Experimental | 1 | 21 | ||||
| Experimental | 1 | 23 | ||||
| Experimental | 1 | 4 | ||||
| Experimental | 1 | 9 |
df <- do.call(rbind, lapply(c("Sexo","Zona","Cor.Raca","Serie","Idade"), FUN = function(x) {
tdat2 <- gdat
tdat2[[x]][is.na(tdat2[[x]])] <- "Not declared"
count_(group_by(tdat2, grupo), x)
}))
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
| grupo | Sexo | n | Zona | Cor.Raca | Serie | Idade |
|---|---|---|---|---|---|---|
| Controle | F | 367 | ||||
| Controle | M | 386 | ||||
| Experimental | F | 502 | ||||
| Experimental | M | 515 | ||||
| Controle | 208 | Not declared | ||||
| Controle | 343 | Rural | ||||
| Controle | 202 | Urbana | ||||
| Experimental | 315 | Not declared | ||||
| Experimental | 421 | Rural | ||||
| Experimental | 281 | Urbana | ||||
| Controle | 76 | Branca | ||||
| Controle | 20 | Indígena | ||||
| Controle | 404 | Not declared | ||||
| Controle | 252 | Parda | ||||
| Controle | 1 | Preta | ||||
| Experimental | 2 | Amarela | ||||
| Experimental | 89 | Branca | ||||
| Experimental | 22 | Indígena | ||||
| Experimental | 610 | Not declared | ||||
| Experimental | 293 | Parda | ||||
| Experimental | 1 | Preta | ||||
| Controle | 213 | 6 ano | ||||
| Controle | 209 | 7 ano | ||||
| Controle | 158 | 8 ano | ||||
| Controle | 173 | 9 ano | ||||
| Experimental | 245 | 6 ano | ||||
| Experimental | 277 | 7 ano | ||||
| Experimental | 253 | 8 ano | ||||
| Experimental | 242 | 9 ano | ||||
| Controle | 112 | 11 | ||||
| Controle | 161 | 12 | ||||
| Controle | 181 | 13 | ||||
| Controle | 166 | 14 | ||||
| Controle | 87 | 15 | ||||
| Controle | 30 | 16 | ||||
| Controle | 7 | 17 | ||||
| Controle | 5 | 18 | ||||
| Controle | 1 | 20 | ||||
| Controle | 1 | 21 | ||||
| Controle | 1 | 22 | ||||
| Controle | 1 | 34 | ||||
| Experimental | 1 | 1 | ||||
| Experimental | 157 | 11 | ||||
| Experimental | 214 | 12 | ||||
| Experimental | 220 | 13 | ||||
| Experimental | 218 | 14 | ||||
| Experimental | 124 | 15 | ||||
| Experimental | 50 | 16 | ||||
| Experimental | 19 | 17 | ||||
| Experimental | 6 | 18 | ||||
| Experimental | 3 | 19 | ||||
| Experimental | 1 | 20 | ||||
| Experimental | 1 | 21 | ||||
| Experimental | 1 | 23 | ||||
| Experimental | 1 | 4 | ||||
| Experimental | 1 | 9 |
df <- do.call(rbind, lapply(c("Sexo","Zona","Cor.Raca","Serie","Idade"), FUN = function(x) {
tdat2 <- gdat[gdat$id %in% dat$id,]
tdat2[[x]][is.na(tdat2[[x]])] <- "Not declared"
count_(group_by(tdat2, grupo), x)
}))
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
| grupo | Sexo | n | Zona | Cor.Raca | Serie | Idade |
|---|---|---|---|---|---|---|
| Controle | F | 259 | ||||
| Controle | M | 257 | ||||
| Experimental | F | 368 | ||||
| Experimental | M | 345 | ||||
| Controle | 149 | Not declared | ||||
| Controle | 253 | Rural | ||||
| Controle | 114 | Urbana | ||||
| Experimental | 230 | Not declared | ||||
| Experimental | 294 | Rural | ||||
| Experimental | 189 | Urbana | ||||
| Controle | 54 | Branca | ||||
| Controle | 13 | Indígena | ||||
| Controle | 280 | Not declared | ||||
| Controle | 168 | Parda | ||||
| Controle | 1 | Preta | ||||
| Experimental | 1 | Amarela | ||||
| Experimental | 62 | Branca | ||||
| Experimental | 18 | Indígena | ||||
| Experimental | 430 | Not declared | ||||
| Experimental | 201 | Parda | ||||
| Experimental | 1 | Preta | ||||
| Controle | 146 | 6 ano | ||||
| Controle | 152 | 7 ano | ||||
| Controle | 100 | 8 ano | ||||
| Controle | 118 | 9 ano | ||||
| Experimental | 165 | 6 ano | ||||
| Experimental | 196 | 7 ano | ||||
| Experimental | 181 | 8 ano | ||||
| Experimental | 171 | 9 ano | ||||
| Controle | 88 | 11 | ||||
| Controle | 122 | 12 | ||||
| Controle | 125 | 13 | ||||
| Controle | 113 | 14 | ||||
| Controle | 43 | 15 | ||||
| Controle | 17 | 16 | ||||
| Controle | 5 | 17 | ||||
| Controle | 2 | 18 | ||||
| Controle | 1 | 34 | ||||
| Experimental | 119 | 11 | ||||
| Experimental | 155 | 12 | ||||
| Experimental | 159 | 13 | ||||
| Experimental | 155 | 14 | ||||
| Experimental | 80 | 15 | ||||
| Experimental | 27 | 16 | ||||
| Experimental | 9 | 17 | ||||
| Experimental | 3 | 18 | ||||
| Experimental | 1 | 19 | ||||
| Experimental | 1 | 20 | ||||
| Experimental | 1 | 21 | ||||
| Experimental | 1 | 23 | ||||
| Experimental | 1 | 4 | ||||
| Experimental | 1 | 9 |
df <- do.call(rbind, lapply(names(ldat), FUN = function(tname) {
dat2 <- ldat[[tname]]
data.frame(
"For Analysis of ANCOVA" = tname,
do.call(rbind, lapply(c("Sexo","Zona","Cor.Raca","Serie","Idade"), FUN = function(x) {
tdat2 <- gdat[gdat$id %in% dat2$id,]
tdat2[[x]][is.na(tdat2[[x]])] <- "Not declared"
count_(group_by(tdat2, grupo), x)
})))
}))
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: `count_()` was deprecated in dplyr 0.7.0.
## ℹ Please use `count()` instead.
## ℹ See vignette('programming') for more help
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
| For.Analysis.of.ANCOVA | grupo | Sexo | n | Zona | Cor.Raca | Serie | Idade |
|---|---|---|---|---|---|---|---|
| grupo | Controle | F | 258 | ||||
| grupo | Controle | M | 257 | ||||
| grupo | Experimental | F | 368 | ||||
| grupo | Experimental | M | 345 | ||||
| grupo | Controle | 149 | Not declared | ||||
| grupo | Controle | 252 | Rural | ||||
| grupo | Controle | 114 | Urbana | ||||
| grupo | Experimental | 230 | Not declared | ||||
| grupo | Experimental | 294 | Rural | ||||
| grupo | Experimental | 189 | Urbana | ||||
| grupo | Controle | 54 | Branca | ||||
| grupo | Controle | 13 | Indígena | ||||
| grupo | Controle | 279 | Not declared | ||||
| grupo | Controle | 168 | Parda | ||||
| grupo | Controle | 1 | Preta | ||||
| grupo | Experimental | 1 | Amarela | ||||
| grupo | Experimental | 62 | Branca | ||||
| grupo | Experimental | 18 | Indígena | ||||
| grupo | Experimental | 430 | Not declared | ||||
| grupo | Experimental | 201 | Parda | ||||
| grupo | Experimental | 1 | Preta | ||||
| grupo | Controle | 146 | 6 ano | ||||
| grupo | Controle | 152 | 7 ano | ||||
| grupo | Controle | 100 | 8 ano | ||||
| grupo | Controle | 117 | 9 ano | ||||
| grupo | Experimental | 165 | 6 ano | ||||
| grupo | Experimental | 196 | 7 ano | ||||
| grupo | Experimental | 181 | 8 ano | ||||
| grupo | Experimental | 171 | 9 ano | ||||
| grupo | Controle | 88 | 11 | ||||
| grupo | Controle | 122 | 12 | ||||
| grupo | Controle | 125 | 13 | ||||
| grupo | Controle | 113 | 14 | ||||
| grupo | Controle | 43 | 15 | ||||
| grupo | Controle | 17 | 16 | ||||
| grupo | Controle | 5 | 17 | ||||
| grupo | Controle | 1 | 18 | ||||
| grupo | Controle | 1 | 34 | ||||
| grupo | Experimental | 119 | 11 | ||||
| grupo | Experimental | 155 | 12 | ||||
| grupo | Experimental | 159 | 13 | ||||
| grupo | Experimental | 155 | 14 | ||||
| grupo | Experimental | 80 | 15 | ||||
| grupo | Experimental | 27 | 16 | ||||
| grupo | Experimental | 9 | 17 | ||||
| grupo | Experimental | 3 | 18 | ||||
| grupo | Experimental | 1 | 19 | ||||
| grupo | Experimental | 1 | 20 | ||||
| grupo | Experimental | 1 | 21 | ||||
| grupo | Experimental | 1 | 23 | ||||
| grupo | Experimental | 1 | 4 | ||||
| grupo | Experimental | 1 | 9 | ||||
| grupo:Sexo | Controle | F | 258 | ||||
| grupo:Sexo | Controle | M | 257 | ||||
| grupo:Sexo | Experimental | F | 368 | ||||
| grupo:Sexo | Experimental | M | 345 | ||||
| grupo:Sexo | Controle | 149 | Not declared | ||||
| grupo:Sexo | Controle | 252 | Rural | ||||
| grupo:Sexo | Controle | 114 | Urbana | ||||
| grupo:Sexo | Experimental | 230 | Not declared | ||||
| grupo:Sexo | Experimental | 294 | Rural | ||||
| grupo:Sexo | Experimental | 189 | Urbana | ||||
| grupo:Sexo | Controle | 54 | Branca | ||||
| grupo:Sexo | Controle | 13 | Indígena | ||||
| grupo:Sexo | Controle | 279 | Not declared | ||||
| grupo:Sexo | Controle | 168 | Parda | ||||
| grupo:Sexo | Controle | 1 | Preta | ||||
| grupo:Sexo | Experimental | 1 | Amarela | ||||
| grupo:Sexo | Experimental | 62 | Branca | ||||
| grupo:Sexo | Experimental | 18 | Indígena | ||||
| grupo:Sexo | Experimental | 430 | Not declared | ||||
| grupo:Sexo | Experimental | 201 | Parda | ||||
| grupo:Sexo | Experimental | 1 | Preta | ||||
| grupo:Sexo | Controle | 146 | 6 ano | ||||
| grupo:Sexo | Controle | 152 | 7 ano | ||||
| grupo:Sexo | Controle | 100 | 8 ano | ||||
| grupo:Sexo | Controle | 117 | 9 ano | ||||
| grupo:Sexo | Experimental | 165 | 6 ano | ||||
| grupo:Sexo | Experimental | 196 | 7 ano | ||||
| grupo:Sexo | Experimental | 181 | 8 ano | ||||
| grupo:Sexo | Experimental | 171 | 9 ano | ||||
| grupo:Sexo | Controle | 88 | 11 | ||||
| grupo:Sexo | Controle | 122 | 12 | ||||
| grupo:Sexo | Controle | 125 | 13 | ||||
| grupo:Sexo | Controle | 113 | 14 | ||||
| grupo:Sexo | Controle | 43 | 15 | ||||
| grupo:Sexo | Controle | 17 | 16 | ||||
| grupo:Sexo | Controle | 5 | 17 | ||||
| grupo:Sexo | Controle | 1 | 18 | ||||
| grupo:Sexo | Controle | 1 | 34 | ||||
| grupo:Sexo | Experimental | 119 | 11 | ||||
| grupo:Sexo | Experimental | 155 | 12 | ||||
| grupo:Sexo | Experimental | 159 | 13 | ||||
| grupo:Sexo | Experimental | 155 | 14 | ||||
| grupo:Sexo | Experimental | 80 | 15 | ||||
| grupo:Sexo | Experimental | 27 | 16 | ||||
| grupo:Sexo | Experimental | 9 | 17 | ||||
| grupo:Sexo | Experimental | 3 | 18 | ||||
| grupo:Sexo | Experimental | 1 | 19 | ||||
| grupo:Sexo | Experimental | 1 | 20 | ||||
| grupo:Sexo | Experimental | 1 | 21 | ||||
| grupo:Sexo | Experimental | 1 | 23 | ||||
| grupo:Sexo | Experimental | 1 | 4 | ||||
| grupo:Sexo | Experimental | 1 | 9 | ||||
| grupo:Zona | Controle | F | 183 | ||||
| grupo:Zona | Controle | M | 183 | ||||
| grupo:Zona | Experimental | F | 241 | ||||
| grupo:Zona | Experimental | M | 242 | ||||
| grupo:Zona | Controle | 252 | Rural | ||||
| grupo:Zona | Controle | 114 | Urbana | ||||
| grupo:Zona | Experimental | 294 | Rural | ||||
| grupo:Zona | Experimental | 189 | Urbana | ||||
| grupo:Zona | Controle | 44 | Branca | ||||
| grupo:Zona | Controle | 12 | Indígena | ||||
| grupo:Zona | Controle | 178 | Not declared | ||||
| grupo:Zona | Controle | 131 | Parda | ||||
| grupo:Zona | Controle | 1 | Preta | ||||
| grupo:Zona | Experimental | 49 | Branca | ||||
| grupo:Zona | Experimental | 17 | Indígena | ||||
| grupo:Zona | Experimental | 250 | Not declared | ||||
| grupo:Zona | Experimental | 166 | Parda | ||||
| grupo:Zona | Experimental | 1 | Preta | ||||
| grupo:Zona | Controle | 125 | 6 ano | ||||
| grupo:Zona | Controle | 127 | 7 ano | ||||
| grupo:Zona | Controle | 56 | 8 ano | ||||
| grupo:Zona | Controle | 58 | 9 ano | ||||
| grupo:Zona | Experimental | 123 | 6 ano | ||||
| grupo:Zona | Experimental | 167 | 7 ano | ||||
| grupo:Zona | Experimental | 89 | 8 ano | ||||
| grupo:Zona | Experimental | 104 | 9 ano | ||||
| grupo:Zona | Controle | 76 | 11 | ||||
| grupo:Zona | Controle | 108 | 12 | ||||
| grupo:Zona | Controle | 82 | 13 | ||||
| grupo:Zona | Controle | 59 | 14 | ||||
| grupo:Zona | Controle | 27 | 15 | ||||
| grupo:Zona | Controle | 10 | 16 | ||||
| grupo:Zona | Controle | 2 | 17 | ||||
| grupo:Zona | Controle | 1 | 18 | ||||
| grupo:Zona | Controle | 1 | 34 | ||||
| grupo:Zona | Experimental | 89 | 11 | ||||
| grupo:Zona | Experimental | 136 | 12 | ||||
| grupo:Zona | Experimental | 93 | 13 | ||||
| grupo:Zona | Experimental | 84 | 14 | ||||
| grupo:Zona | Experimental | 53 | 15 | ||||
| grupo:Zona | Experimental | 14 | 16 | ||||
| grupo:Zona | Experimental | 8 | 17 | ||||
| grupo:Zona | Experimental | 1 | 18 | ||||
| grupo:Zona | Experimental | 1 | 19 | ||||
| grupo:Zona | Experimental | 1 | 20 | ||||
| grupo:Zona | Experimental | 1 | 21 | ||||
| grupo:Zona | Experimental | 1 | 23 | ||||
| grupo:Zona | Experimental | 1 | 9 | ||||
| grupo:Cor.Raca | Controle | F | 119 | ||||
| grupo:Cor.Raca | Controle | M | 116 | ||||
| grupo:Cor.Raca | Experimental | F | 138 | ||||
| grupo:Cor.Raca | Experimental | M | 143 | ||||
| grupo:Cor.Raca | Controle | 48 | Not declared | ||||
| grupo:Cor.Raca | Controle | 145 | Rural | ||||
| grupo:Cor.Raca | Controle | 42 | Urbana | ||||
| grupo:Cor.Raca | Experimental | 49 | Not declared | ||||
| grupo:Cor.Raca | Experimental | 185 | Rural | ||||
| grupo:Cor.Raca | Experimental | 47 | Urbana | ||||
| grupo:Cor.Raca | Controle | 54 | Branca | ||||
| grupo:Cor.Raca | Controle | 13 | Indígena | ||||
| grupo:Cor.Raca | Controle | 168 | Parda | ||||
| grupo:Cor.Raca | Experimental | 62 | Branca | ||||
| grupo:Cor.Raca | Experimental | 18 | Indígena | ||||
| grupo:Cor.Raca | Experimental | 201 | Parda | ||||
| grupo:Cor.Raca | Controle | 66 | 6 ano | ||||
| grupo:Cor.Raca | Controle | 74 | 7 ano | ||||
| grupo:Cor.Raca | Controle | 42 | 8 ano | ||||
| grupo:Cor.Raca | Controle | 53 | 9 ano | ||||
| grupo:Cor.Raca | Experimental | 75 | 6 ano | ||||
| grupo:Cor.Raca | Experimental | 66 | 7 ano | ||||
| grupo:Cor.Raca | Experimental | 72 | 8 ano | ||||
| grupo:Cor.Raca | Experimental | 68 | 9 ano | ||||
| grupo:Cor.Raca | Controle | 48 | 11 | ||||
| grupo:Cor.Raca | Controle | 53 | 12 | ||||
| grupo:Cor.Raca | Controle | 58 | 13 | ||||
| grupo:Cor.Raca | Controle | 54 | 14 | ||||
| grupo:Cor.Raca | Controle | 12 | 15 | ||||
| grupo:Cor.Raca | Controle | 7 | 16 | ||||
| grupo:Cor.Raca | Controle | 3 | 17 | ||||
| grupo:Cor.Raca | Experimental | 57 | 11 | ||||
| grupo:Cor.Raca | Experimental | 49 | 12 | ||||
| grupo:Cor.Raca | Experimental | 55 | 13 | ||||
| grupo:Cor.Raca | Experimental | 61 | 14 | ||||
| grupo:Cor.Raca | Experimental | 35 | 15 | ||||
| grupo:Cor.Raca | Experimental | 17 | 16 | ||||
| grupo:Cor.Raca | Experimental | 4 | 17 | ||||
| grupo:Cor.Raca | Experimental | 1 | 18 | ||||
| grupo:Cor.Raca | Experimental | 1 | 21 | ||||
| grupo:Cor.Raca | Experimental | 1 | 23 | ||||
| grupo:Serie | Controle | F | 258 | ||||
| grupo:Serie | Controle | M | 257 | ||||
| grupo:Serie | Experimental | F | 368 | ||||
| grupo:Serie | Experimental | M | 345 | ||||
| grupo:Serie | Controle | 149 | Not declared | ||||
| grupo:Serie | Controle | 252 | Rural | ||||
| grupo:Serie | Controle | 114 | Urbana | ||||
| grupo:Serie | Experimental | 230 | Not declared | ||||
| grupo:Serie | Experimental | 294 | Rural | ||||
| grupo:Serie | Experimental | 189 | Urbana | ||||
| grupo:Serie | Controle | 54 | Branca | ||||
| grupo:Serie | Controle | 13 | Indígena | ||||
| grupo:Serie | Controle | 279 | Not declared | ||||
| grupo:Serie | Controle | 168 | Parda | ||||
| grupo:Serie | Controle | 1 | Preta | ||||
| grupo:Serie | Experimental | 1 | Amarela | ||||
| grupo:Serie | Experimental | 62 | Branca | ||||
| grupo:Serie | Experimental | 18 | Indígena | ||||
| grupo:Serie | Experimental | 430 | Not declared | ||||
| grupo:Serie | Experimental | 201 | Parda | ||||
| grupo:Serie | Experimental | 1 | Preta | ||||
| grupo:Serie | Controle | 146 | 6 ano | ||||
| grupo:Serie | Controle | 152 | 7 ano | ||||
| grupo:Serie | Controle | 100 | 8 ano | ||||
| grupo:Serie | Controle | 117 | 9 ano | ||||
| grupo:Serie | Experimental | 165 | 6 ano | ||||
| grupo:Serie | Experimental | 196 | 7 ano | ||||
| grupo:Serie | Experimental | 181 | 8 ano | ||||
| grupo:Serie | Experimental | 171 | 9 ano | ||||
| grupo:Serie | Controle | 88 | 11 | ||||
| grupo:Serie | Controle | 122 | 12 | ||||
| grupo:Serie | Controle | 125 | 13 | ||||
| grupo:Serie | Controle | 113 | 14 | ||||
| grupo:Serie | Controle | 43 | 15 | ||||
| grupo:Serie | Controle | 17 | 16 | ||||
| grupo:Serie | Controle | 5 | 17 | ||||
| grupo:Serie | Controle | 1 | 18 | ||||
| grupo:Serie | Controle | 1 | 34 | ||||
| grupo:Serie | Experimental | 119 | 11 | ||||
| grupo:Serie | Experimental | 155 | 12 | ||||
| grupo:Serie | Experimental | 159 | 13 | ||||
| grupo:Serie | Experimental | 155 | 14 | ||||
| grupo:Serie | Experimental | 80 | 15 | ||||
| grupo:Serie | Experimental | 27 | 16 | ||||
| grupo:Serie | Experimental | 9 | 17 | ||||
| grupo:Serie | Experimental | 3 | 18 | ||||
| grupo:Serie | Experimental | 1 | 19 | ||||
| grupo:Serie | Experimental | 1 | 20 | ||||
| grupo:Serie | Experimental | 1 | 21 | ||||
| grupo:Serie | Experimental | 1 | 23 | ||||
| grupo:Serie | Experimental | 1 | 4 | ||||
| grupo:Serie | Experimental | 1 | 9 | ||||
| grupo:vocab.teach.quintile | Controle | F | 258 | ||||
| grupo:vocab.teach.quintile | Controle | M | 257 | ||||
| grupo:vocab.teach.quintile | Experimental | F | 368 | ||||
| grupo:vocab.teach.quintile | Experimental | M | 345 | ||||
| grupo:vocab.teach.quintile | Controle | 149 | Not declared | ||||
| grupo:vocab.teach.quintile | Controle | 252 | Rural | ||||
| grupo:vocab.teach.quintile | Controle | 114 | Urbana | ||||
| grupo:vocab.teach.quintile | Experimental | 230 | Not declared | ||||
| grupo:vocab.teach.quintile | Experimental | 294 | Rural | ||||
| grupo:vocab.teach.quintile | Experimental | 189 | Urbana | ||||
| grupo:vocab.teach.quintile | Controle | 54 | Branca | ||||
| grupo:vocab.teach.quintile | Controle | 13 | Indígena | ||||
| grupo:vocab.teach.quintile | Controle | 279 | Not declared | ||||
| grupo:vocab.teach.quintile | Controle | 168 | Parda | ||||
| grupo:vocab.teach.quintile | Controle | 1 | Preta | ||||
| grupo:vocab.teach.quintile | Experimental | 1 | Amarela | ||||
| grupo:vocab.teach.quintile | Experimental | 62 | Branca | ||||
| grupo:vocab.teach.quintile | Experimental | 18 | Indígena | ||||
| grupo:vocab.teach.quintile | Experimental | 430 | Not declared | ||||
| grupo:vocab.teach.quintile | Experimental | 201 | Parda | ||||
| grupo:vocab.teach.quintile | Experimental | 1 | Preta | ||||
| grupo:vocab.teach.quintile | Controle | 146 | 6 ano | ||||
| grupo:vocab.teach.quintile | Controle | 152 | 7 ano | ||||
| grupo:vocab.teach.quintile | Controle | 100 | 8 ano | ||||
| grupo:vocab.teach.quintile | Controle | 117 | 9 ano | ||||
| grupo:vocab.teach.quintile | Experimental | 165 | 6 ano | ||||
| grupo:vocab.teach.quintile | Experimental | 196 | 7 ano | ||||
| grupo:vocab.teach.quintile | Experimental | 181 | 8 ano | ||||
| grupo:vocab.teach.quintile | Experimental | 171 | 9 ano | ||||
| grupo:vocab.teach.quintile | Controle | 88 | 11 | ||||
| grupo:vocab.teach.quintile | Controle | 122 | 12 | ||||
| grupo:vocab.teach.quintile | Controle | 125 | 13 | ||||
| grupo:vocab.teach.quintile | Controle | 113 | 14 | ||||
| grupo:vocab.teach.quintile | Controle | 43 | 15 | ||||
| grupo:vocab.teach.quintile | Controle | 17 | 16 | ||||
| grupo:vocab.teach.quintile | Controle | 5 | 17 | ||||
| grupo:vocab.teach.quintile | Controle | 1 | 18 | ||||
| grupo:vocab.teach.quintile | Controle | 1 | 34 | ||||
| grupo:vocab.teach.quintile | Experimental | 119 | 11 | ||||
| grupo:vocab.teach.quintile | Experimental | 155 | 12 | ||||
| grupo:vocab.teach.quintile | Experimental | 159 | 13 | ||||
| grupo:vocab.teach.quintile | Experimental | 155 | 14 | ||||
| grupo:vocab.teach.quintile | Experimental | 80 | 15 | ||||
| grupo:vocab.teach.quintile | Experimental | 27 | 16 | ||||
| grupo:vocab.teach.quintile | Experimental | 9 | 17 | ||||
| grupo:vocab.teach.quintile | Experimental | 3 | 18 | ||||
| grupo:vocab.teach.quintile | Experimental | 1 | 19 | ||||
| grupo:vocab.teach.quintile | Experimental | 1 | 20 | ||||
| grupo:vocab.teach.quintile | Experimental | 1 | 21 | ||||
| grupo:vocab.teach.quintile | Experimental | 1 | 23 | ||||
| grupo:vocab.teach.quintile | Experimental | 1 | 4 | ||||
| grupo:vocab.teach.quintile | Experimental | 1 | 9 |